Proposed Decision Tree Research Articles

Useful Life of Prescribed Fires in a Southern Mediterranean Basin: An Application to Pinus pinaster Stands in the Sierra Morena Range

Prescribed fire is a globally relevant fuel treatment for surface fuel management and wildfire hazard reduction. However, Mediterranean ecosystems are adapted to low and moderate fires; hence, the useful life of prescribed fires is limited. Useful life is defined as the effective rotation length of prescribed fires to mitigate fire spread based on critical surface intensity for crown combustion. In this sense, the useful life of a prescribed fire focuses on surface fuel dynamics and its potential fire behavior. In Pinus pinaster stands, the useful life can be established between 0 and 4 years. Canopy base height, time elapsed from the burning, postfire precipitation, and fine fuel moisture content during the burning were identified as the most important variables in postburn fuel dynamics. Other stand characteristics and postfire precipitation can improve the fine fuel and live fuel dynamics models. Our findings support prescribed fires as an effective fuel treatment in the medium term for forest fire prevention, according to stand characteristics and burning implementation conditions. In this sense, forest managers can use the proposed decision tree to identify the useful life of each prescribed fire based on fine fuel moisture content during burning implementation.

A new structure of decision tree based on oriented edges gradient map for circles detection and the analysis of nano-particles

In this paper, we propose a new approach to detect circles and nano-particles based on an oriented-edges gradient map and a decision tree. The decision tree is calculated from geometric constraints based on particular right triangles inscribed in a circle. Use of the proposed accumulator and dynamic storage matrix radii shows the robustness of our algorithm in terms of results and execution time. This robustness can also be enhanced in the event of prior knowledge. Indeed, we can enable or disable intermediate nodes or a part of nodes of the proposed decision tree to strengthen both the detection results and the execution time of the algorithm. Our approach makes it possible to detect circles and analyse the distribution of the nano-particles which is evaluated using four databases which include TEM, synthetic, real and complex images.

A New Hybrid Teaching Model for a Psychology Course

The current traditional college psychology courses only pay attention to the prevention and correction of the psychological problems of college students, but neglect to let students have a good positive emotion to learn, which is not conducive to the development of psychological potential of students, thus psychology learning cannot achieve the expected results. Based on the psychology blended teaching theory, combined with the entrepreneurial education of college students, a set of PDCA (Plan-Do-Check-Action) teaching mode which adapts to the development needs of "Internet education" is designed, and formed several new methods of teaching, such as "integration of online and offline classroom teaching". In addition, a new performance prediction model based on graph convolution neural network is proposed decision tree data mining methods are used to predict student performance, by extracting user information and high-dimensional information of the knowledge graph, this mode is conducive to platform managers to understand students’ conditions in time and improve teaching quality. This teaching mode dynamically analyzes the internal relationships among learners, teachers, online classrooms, offline classrooms and it can find problems early and provide solutions. It is conducive to improving students' academic performance and learning enthusiasm, and enhancing the ability of college students.

Prediction of Recurrence by Machine Learning in Salivary Gland Cancer Patients After Adjuvant (Chemo)Radiotherapy.

To investigate survival outcomes and recurrence patterns using machine learning in patients with salivary gland malignant tumor (SGMT) undergoing adjuvant chemoradiotherapy (CRT). Consecutive SGMT patients were identified, and a data set included nine predictor variables and a dependent variable [disease-free survival (DFS) event] was standardized. The open-source R software was used. Survival outcomes were estimated by the Kaplan-Meier method. The random forest approach was used to select the important explanatory variables. A classification tree that optimally partitioned SGMT patients with different DFS rates was built. In total, 54 SGMT patients were included in the final analysis. Five-year DFS was 62.1%. The top two important variables identified were pathologic node (pN) and pathologic tumor (pT). Based on these explanatory variables, patients were partitioned in three groups, including pN0, pT1-2 pN+ and pT3-4 pN+ with 26%, 38% and 75% probability of recurrence, respectively. Accordingly, 5-year DFS rates were 73.7%, 57.1% and 34.3%, respectively. The proposed decision tree algorithm is an appropriate tool to partition SGMT patients. It can guide decision-making and future research in the SGMT field.

A Novel Classification Strategy to Distinguish Five Levels of Pain Using the EEG Signal Features

Research studies have tried to extract pain-related features from electroencephalogram(EEG) signals for quantitative measuring of pain. In this study, we go one step further to measure three/five levels of pain by proposing efficient EEG processing steps in conjunction with a new classification strategy. 24 healthy subjects voluntarily performed the cold pressor test while their EEGs were recorded. First, the EEGs were decomposed by independent component analysis and the artifact sources were removed. Among the remained sources, pain-related sources, were chosen according to an adopted information criterion. Next, the EEGs were reconstructed by projecting back the selected sources. Then, grand average brain maps of train subjects were estimated for each pain level over the Alpha(8-12 Hz) and Delta(0.5-4 Hz) bands. By tracing the brain maps' changes over different pain levels, the structure of the proposed decision tree was formed. To enrich the feature set, we also extracted other EEG features. For each decision node, a specific subset of features was selected by sequential forward selection method. Considering k-nearest neighbor(KNN) as the decision marker,the classification accuracies for the three and five pain levels was determined 80 ± 5 and 60 ± 5 percent, respectively while by choosing support vector machine(SVM), the results improved up to 83 ± 5 and 62 ± 6 percent,respectively.

A decision tree for building IT applications

Blockchain technology has gained increasing attention from research and industry over the recent years. It allows implementing in its environment the smart-contracts technology which is used to automate and execute agreements between users. The blockchain is proposed today as a new technical infrastructure for several types of IT applications. This interest is mainly due to its core property that allows two users to perform transactions without going through a Trusted Third Party, while offering a transparent and fully protected data storage. However, a blockchain comes along a number of other intrinsic properties, which may not be suitable or beneficial in all the envisaged application cases. Consequently, we propose in this paper to design a new tool which is “a decision tree” that allows identifying when a blockchain may be the appropriate technical infrastructure for a given IT application, and when another classical system (centralized or distributed peer-to-peer) is more adapted. The proposed decision tree allows also identifying whether or not it is necessary to use the smart-contracts technology.

A decision tree framework to support design, operation, and performance assessment of constructed wetlands for the removal of emerging organic contaminants

There is an increasing focus on research related to the removal of emerging organic contaminants (EOCs) from wastewater by using constructed wetlands (CWs). However, research is lacking on translating the available scientific evidence into decision support tools. In this paper, a novel decision tree framework is developed and demonstrated. The proposed framework consists of five steps: (1) generate a list of EOCs by the analysis of the wastewater; (2) select the best type of CW for each of the selected EOCs; (3) select a final type of CW for the removal of the selected EOCs; (4) identify detailed design and operational features of the proposed CW such as, depth, area, plants, support matrix, hydraulic loading rate, organic loading rate, and hydraulic retention time; and (5) assess the expected removal efficiency of EOCs in the selected CW. A novel decision support tool, named as DTFT-CW, was developed to generate data and information for the application of the proposed decision tree framework. DTFT-CW (given as a supplementary material) was developed using Microsoft Excel 2016 to support decisions on the design, operation, and performance of CWs for the removal of 59 EOCs (33 pharmaceuticals-PhCs, 15 personal care products-PCPs, and 11 steroidal hormones-SHs). The paper demonstrates the usefulness of the developed decision-making tools by considering 19 EOCs (13 PhCs, one PCPs, and five SHs) as an example, which pose high environmental risk and are on the European Union watch list (six of the 19 EOCs). An integrated design of HCW (combining vertical flow CW, horizontal flow CW-HFCW, and free water surface CW) is recommended for the treatment of multiple EOCs instead of a single type of CW such as HFCW that is most widely used in practice. The proposed tools could be useful for decision makers such as policy makers, design engineers, and researchers.

A Simple Decision Tree Suited for Identification of Early Oral Drug Candidates With Likely Pharmacokinetic Nonlinearity by Intestinal CYP3A Saturation

To identify oral drugs that likely display nonlinear pharmacokinetics due to saturable metabolism by intestinal CYP3A, our previous report using CYP3A substrate drugs proposed an approach using thresholds for the linear index number (LIN3A = dose/Km; Km, Michaelis-Menten constant for CYP3A) and the intestinal availability (FaFg). Here, we aimed to extend the validity of the previous approach using both CYP3A substrate and non-substrate drugs and to devise a decision tree suited for early drug candidates using in vitro metabolic intrinsic clearance (CLint, vitro) instead of FaFg. Out of 152 oral drugs (including 136 drugs approved in Japan, US or both), type I nonlinearity (in which systemic drug exposure increases in a more than dose-proportional manner) was noted with 82 drugs (54%), among which 58 drugs were identified as CYP3A substrates based on public information. Based on practical feasibility, 41 drugs were selected from CYP3A substrates and subjected to in-house metabolic assessment. The results were used to determine the thresholds for CLint, vitro (0.45 μL/min/pmol CYP3A4) and LIN3A (1.0 L). For four drugs incorrectly predicted, potential mechanisms were looked up. Overall, our proposed decision tree may aid in the identification of early drug candidates with intestinal CYP3A-derived type I nonlinearity.

Evidence-based strategies for interdental cleaning: a practical decision tree and review of the literature.

<p>Objectives: Although several studies have investigated the effectiveness of various interdental cleaning devices, there is a need for an evidence-based synopsis for clinicians to customize interdental hygiene instructions and provide specific devices for each patient. This literature review aims to establish an evidence-based decision-making tree recommending individualized approaches to interdental cleaning based on embrasure size and patient-specific conditions.<br /> Data sources: Specific keywords related to interdental cleaning were used to search and identify the existing literature in PubMed and the Cochrane Library. Through a series of review processes, qualifying studies were identified and assessed with respect to the inclusion criteria to establish the decision tree.<br /> Results: A total of 27 studies were included to support a decision tree. Traditional dental floss continues to remain the first choice for individuals of high motivation and good ?manual dexterity with type I closed embrasures. For individuals with closed embrasures, but lack of motivation and/or dexterity, the use of easy flossers, soft picks, oral irrigation, and small (0.6 to 0.7 mm) interdental brushes are alternatives. For individuals with type II and type III open embrasure spaces, an interdental brush has the highest evidence for its effectiveness to remove interdental plaque. However, two studies showed that residual plaque could be found over lingual embrasur?es and thus lingual approach of the interdental brush is sometimes needed. The use of gum stimulators and/or woodsticks continues to be supported when significant gingival inflammation is present.<br /> Conclusion: Each patient should be individually assessed and given tailored oral hygiene home care instructions for the most effective outcomes. The proposed decision tree provides clinicians with an evidence-based guideline to help customize the use of interdental cleaning devices for each patient.</p>.

Insights into the estimation of hydroxyl radical rate constant of water contaminants in AOP using new smart QSPR models

AbstractInvestigations on the advanced oxidation processes (AOP) express that one of the effective oxidation agents in the degradation of organic pollutants is the aqueous hydroxyl radical. Therefore, the main aim of this work is the determination of the hydroxyl radical rate constant. To this end, three artificial intelligence methods were used to predict this constant for 457 various water pollutants from 27 molecular structures. The modelling results showed that the proposed decision tree model can determine the mentioned parameter with R‐squared values of 0.9264 and mean relative error of 1.479 for the overall data set. In addition, a comprehensive analysis of input parameters showed that Burden eigenvalue has the most impact on the hydroxyl radical rate constant. Finally, different comparisons were carried out between the suggested algorithms and other published approaches in the literature, which showed a high degree of precision for our models.

Application of the Extended Clearance Classification System (ECCS) in Drug Discovery and Development: Selection of Appropriate In Vitro Tools and Clearance Prediction.

In vitro to in vivo extrapolation (IVIVE) to predict human hepatic clearance, including metabolism and transport, requires extensive experimental resources. In addition, there may be technical challenges to measure low clearance values. Therefore, prospective identification of rate-determining step(s) in hepatic clearance through application of the Extended Clearance Classification System (ECCS) could be beneficial for optimal compound characterization. IVIVE for hepatic intrinsic clearance (CLint,h) prediction is conducted for a set of 36 marketed drugs with low-to-high in vivo clearance, which are substrates of metabolic enzymes and active uptake transporters in the liver. The compounds were assigned to the ECCS classes, and CLint,h, estimated with HepatoPac (a micropatterned hepatocyte coculture system), was compared with values calculated based on suspended hepatocyte incubates. An apparent permeability threshold (apical to basal) of 50 nm/s in LLC-PK1 cells proved optimal for ECCS classification. A reasonable performance of the IVIVE for compounds across multiple classes using HepatoPac was achieved (with 2-3-fold error), except for substrates of uptake transporters (class 3b), for which scaling of uptake clearance using plated hepatocytes is more appropriate. Irrespective of the ECCS assignment, metabolic clearance can be estimated well using HepatoPac. The validation and approach elaborated in the present study can result in proposed decision trees for the selection of the optimal in vitro assays guided by ECCS class assignment, to support compound optimization and candidate selection. SIGNIFICANCE STATEMENT: Characterization of the rate-determining step(s) in hepatic elimination could be on the critical path of compound optimization during drug discovery. This study demonstrated that HepatoPac and plated hepatocytes are suitable tools for the estimation of metabolic and active uptake clearance, respectively, for a larger set of marketed drugs, supporting a comprehensive strategy to select optimal in vitro tools and to achieve Extended Clearance Classification System-dependent in vitro to in vivo extrapolation for human clearance prediction.

A novel framework of fuzzy oblique decision tree construction for pattern classification

In this paper, some significant efforts on fuzzy oblique decision tree (FODT) have been done to improve classification accuracy and decrease tree size. Firstly, to eliminate data redundancy and improve classification efficiency, a forward greedy fast feature selection algorithm based on neighborhood rough set (NRS_FS_FAST) is introduced. Then, a new fuzzy rule generation algorithm (FRGA) is proposed to generate fuzzy rules. These fuzzy rules are used to construct leaf nodes for each class in each layer of the FODT. Different from the traditional axis-parallel decision trees and oblique decision trees, the FODT takes dynamic mining fuzzy rules as decision functions. Moreover, the parameter δ, which can control the size of the tree, is optimized by genetic algorithm. Finally, a series of comparative experiments are carried out with five traditional decision trees (C4.5, Best First Tree (BFT), amulti-class alternating decision tree (LAD), Simple Cart (SC), Naive Bayes Tree (NBT)), and recently proposed decision trees (FRDT, HHCART, and FMMDT-HB) on UCI machine learning datasets. The experimental results demonstrate that the FODT exhibits better performance on classification accuracy and tree size than the chosen benchmarks.

Motorsafe: An Android Application for Motorcyclists Using Decision Tree Algorithm

In Vietnam, prohibitions on using the telephone while driving vehicles have effected in a long time. However, that is relatively common and dangerous for traffic participants in reality. Along with the continuous growing of smartphones, using phone while driving is becoming a bad habit and one of the major causes of crashes and accidents of traffic in Vietnam. MotorSafe is a novel application that has been practically implemented as a solution for this problem. From the obtained data of the accelerometer sensor on the smartphone, a proposed decision tree algorithm enables device to recognize the user's status on driving a vehicle. Some necessary tasks are provided that will help drivers focus on driving instead of unnecessary behaviors.

Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads.

Axiomatic fuzzy set theory-based fuzzy oblique decision tree with dynamic mining fuzzy rules

This paper proposes a novel classification technology—fuzzy rule-based oblique decision tree (FRODT). The neighborhood rough sets-based FAST feature selection (NRS_FS_FAST) is first introduced to reduce attributes. In the axiomatic fuzzy set theory framework, the fuzzy rule extraction algorithm is then proposed to dynamically extract fuzzy rules. And these rules are regarded as the decision function during the tree construction. The FRODT is developed by expanding the unique non-leaf node in each layer of the tree, which results in a new tree structure with linguistic interpretation. Moreover, the genetic algorithm is implemented on $$\sigma $$ to obtain the balanced results between classification accuracy and tree size. A series of comparative experiments are carried out with five classical classification algorithms (C4.5, BFT, LAD, SC and NBT), and recently proposed decision tree HHCART on 20 UCI data sets. Experiment results show that the FRODT exhibits better classification performance on accuracy and tree size than those of the rival algorithms.

Ultrasound and Clinical Preoperative Characteristics for Discrimination Between Ovarian Metastatic Colorectal Cancer and Primary Ovarian Cancer: A Case-Control Study.

The aim of this study was to describe the clinical and sonographic features of ovarian metastases originating from colorectal cancer (mCRC), and to discriminate mCRC from primary ovarian cancer (OC). We conducted a multi-institutional, retrospective study of consecutive patients with ovarian mCRC who had undergone ultrasound examination using the International Ovarian Tumor Analysis (IOTA) terminology, with the addition of evaluating signs of necrosis and abdominal staging. A control group included patients with primary OC. Clinical and ultrasound data, subjective assessment (SA), and an assessment of different neoplasias in the adnexa (ADNEX) model were evaluated. Fisher’s exact and Student’s t-tests, the area under the receiver–operating characteristic curve (AUC), and classification and regression trees (CART) were used to conduct statistical analyses. In total, 162 patients (81 with OC and 81 with ovarian mCRC) were included. None of the patients with OC had undergone chemotherapy for CRC in the past, compared with 40% of patients with ovarian mCRC (p < 0.001). The ovarian mCRC tumors were significantly larger, a necrosis sign was more frequently present, and tumors had an irregular wall or were fixed less frequently; ascites, omental cake, and carcinomatosis were less common in mCRC than in primary OC. In a subgroup of patients with ovarian mCRC who had not undergone treatment for CRC in anamnesis, tumors were larger, and had fewer papillations and more locules compared with primary OC. The highest AUC for the discrimination of ovarian mCRC from primary OC was for CART (0.768), followed by SA (0.735) and ADNEX calculated with CA-125 (0.680). Ovarian mCRC and primary OC can be distinguished based on patient anamnesis, ultrasound pattern recognition, a proposed decision tree model, and an ADNEX model with CA-125 levels.

Forecasting the Amount of Blood Ordered in the Obstetrics and Gynaecology Ward with the Data Mining Approach.

Preoperative blood ordering is frequently used in the obstetrics and gynecology ward of university hospitals in Iran, even for surgeries that rarely require blood transfusions. This routine procedure is an inefficient use of resources and rising costs, wasting time and cause shortage for essential patients. So this study was carried out to propose a new optimal system based on data mining techniques for ordering blood. This cross-sectional study examined the number of units cross-matched and transfused during surgery in the obstetrics and gynecology ward from 2013 to 2015. Data was collected for 1097 patients. Statistical analyzing was applied on data to prove that; the current blood ordering was not optimal. So with use of blood indices, C/T ratio, the new blood ordering variable was introduced. Then decision tree was applied on data with use of Rapid miner. Decision tree evaluation measures were rMSE and accuracy. A total of 1097 patients were examined for which 9747 units of blood were ordered. There was a significant difference between the number of cross-matched and transfused units according to all variables. The new method reduced the cross-matched units about 71.50%. The accuracy of proposed decision tree based on new blood ordering variable (according to C/T index) was 96.10%. The effective variables of blood ordered were type of surgery, blood group and amount of hemoglobin. The recent blood ordering variable prevent blood shortages, reduce costs. Excessive blood ordering is common in the obstetrics and gynecology department. According to proper results of new ordering variable, we suggest to apply this procedure in all hospitals in order to reduce extra costs and the optimal management of blood ordering.

Decision trees using local support vector regression models for large datasets

ABSTRACTOur proposed decision trees using local support vector regression models (tSVR, rtSVR) aim to efficiently handle the regression task for large datasets. The learning algorithm tSVR of regression models is done by two main steps. The first one is to construct a decision tree regressor for partitioning the full training dataset into k terminal-nodes (subsets), followed which the second one is to learn the SVR model from each terminal-node to predict the data locally in a parallel way on multi-core computers. The algorithm rtSVR learns the random forest of decision trees with local SVR models for improving the prediction correctness against the tSVR model alone. The performance analysis shows that our algorithms tSVR, rtSVR are efficient in terms of the algorithmic complexity and the generalization ability compared to the classical SVR. The experimental results on five large datasets from UCI repository showed that proposed tSVR and rtSVR algorithms are faster than the standard SVR in training the non-linear regression model from large datasets while achieving the high correctness in the prediction. Typically, the average training time of tSVR and rtSVR are 1282.66 and 482.29 times faster than the standard SVR; Furthermore, tSVR and rtSVR improve 59.43%, 63.70% of the relative prediction correctness compared to the standard SVR.

Extrapolation Factors for Characterizing Freshwater Ecotoxicity Effects

Various environmental and chemical assessment frameworks including ecological risk assessment and life cycle impact assessment aim at evaluating long-term ecotoxicity effects. Chronic test data are reported under the European Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation for various chemicals. However, chronic data are missing for a large fraction of marketed chemicals, for which acute test results are often available. Utilizing acute data requires robust extrapolation factors across effect endpoints, exposure durations, and species groups. We propose a decision tree based on strict criteria for curating and selecting high-quality aquatic ecotoxicity information available in REACH for organic chemicals, to derive a consistent set of generic and species group-specific extrapolation factors. Where ecotoxicity effect data are not available at all, we alternatively provide extrapolations from octanol-water partitioning coefficients as suitable predictor for chemicals with nonpolar narcosis as mode of action. Extrapolation factors range from 0.2 to 7 and are higher when simultaneously extrapolating across effect endpoints and exposure durations. Our results are consistent with previously reported values, while considering more endpoints, providing species group-specific factors, and characterizing uncertainty. Our proposed decision tree can be adapted to curate information from additional data sources as well as data for other environments, such as sediment ecotoxicity. Our approach and robust extrapolation factors help to increase the substance coverage for characterizing ecotoxicity effects across chemical and environmental assessment frameworks. Environ Toxicol Chem 2019;38:2568-2582. © 2019 SETAC.

Decision tree based bridge restoration models for extreme event performance assessment of regional road networks

Bridge restoration models are essential for assessing the functionality of bridges after extreme events, emergency response planning, and evaluating resilience and indirect losses. Most of the existing restoration models lack explicit evaluation of anticipated bridge functionality in terms of traffic restrictions on bridges, which is necessary for realistic road network modeling. Therefore, this paper proposes a decision tree based approach to determine potential traffic restrictions and their durations using empirical and expert opinion survey data on restrictions imposed on bridges after extreme events. The proposed methodology is applied in this paper to obtain seismic restoration models for bridges. Herein, three separate decision trees are developed to determine various traffic restrictions, anticipated for different levels of damage to bridge components, including bridge closure, lane closure, and speed/load restriction. Corresponding to each of the three traffic restriction decision trees, another decision tree is developed which determines the duration of the traffic restriction. The probabilistic seismic performance of a regional highway network in Memphis, Tennessee, is studied using the proposed decision trees. Additionally, HAZUS bridge restoration functions are also applied and the differences in the time evolving bridge level functionality estimates and their impacts on network performance are highlighted.