Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters

There are two notoriously hard problems in cluster analysis, estimating the number of clusters, and checking whether the population to be clustered is not actually homogeneous. Given a dataset, a clustering method and a cluster validation index, this paper proposes to set up null models that capture structural features of the data that cannot be interpreted as indicating clustering. Artificial datasets are sampled from the null model with parameters estimated from the original dataset. This can be used for testing the null hypothesis of a homogeneous population against a clustering alternative. It can also be used to calibrate the validation index for estimating the number of clusters, by taking into account the expected distribution of the index under the null model for any given number of clusters. The approach is illustrated by three examples, involving various different clustering techniques (partitioning around medoids, hierarchical methods, a Gaussian mixture model), validation indexes (average silhouette width, prediction strength and BIC), and issues such as mixed-type data, temporal and spatial autocorrelation

A hybrid of fuzzy theory and quadratic function for estimating and refining transmission map

© TÜBİTAK In photographs captured in outdoor environments, particles in the air cause light attenuation and degrade image quality. This effect is especially obvious in hazy environments. In this study, a fuzzy theory is proposed to estimate the transmission map of a single image. To overcome the problem of oversaturation in dehazed images, a quadratic-function-based method is proposed to refine the transmission map. In addition, the color vector of the atmospheric light is estimated using the top 1% of the brightest light area. Finally, the dehazed image is reconstructed using the transmission map and the estimated atmospheric light. Experimental results demonstrate that the proposed hybrid method performs better than the other existing methods in terms of color oversaturation, visibility, and quantitative evaluation

Photoacoustic Imaging for Noninvasive Periodontal Probing Depth Measurements.

The periodontal probe is the gold standard tool for periodontal examinations, including probing depth measurements, but is limited by systematic and random errors. Here, we used photoacoustic ultrasound for high-spatial resolution imaging of probing depths. Specific contrast from dental pockets was achieved with food-grade cuttlefish ink as a contrast medium. Here, 39 porcine teeth (12 teeth with artificially deeper pockets) were treated with the contrast agent, and the probing depths were measured with novel photoacoustic imaging and a Williams periodontal probe. There were statistically significant differences between the 2 measurement approaches for distal, lingual, and buccal sites but not mesial. Bland-Altman analysis revealed that all bias values were < ±0.25 mm, and the coefficients of variation for 5 replicates were <11%. The photoacoustic imaging approach also offered 0.01-mm precision and could cover the entire pocket, as opposed to the probe-based approach, which is limited to only a few sites. This report is the first to use photoacoustic imaging for probing depth measurements with potential implications to the dental field, including tools for automated dental examinations or noninvasive examinations

Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data

Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved

Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

© The Authors, published by EDP Sciences, 2018. In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods

Enhancing resilience by reducing critical load loss via an emergent trading framework considering possible resources isolation under typhoon

Leveraging distributed resources to enhance distribution network (DN) resilience is an effective measure in response to natural disasters. However, the willingness and economy of distributed resources are typically ignored. To address this issue, this paper proposes an emergent trading framework that uses parking lots (PLs) as resources to provide power support to critical loads (CLs) in a blackout due to typhoons. In this trading framework, an evolutionary Stackelberg game-based trading model is established to consider maximizing all stakeholders' economic benefits, considering possible resources isolation under typical fault scenarios caused by typhoons, and a benefit allocation mechanism is proposed for all stakeholders to motivate all stakeholders to participate in the trading. This framework allows that critical loads could reduce their load loss, parking lots could receive adequate compensation to stimulate them to participate in the trading, and distribution utility could ensure its economic benefits. Furthermore, an iterative evolutionary-Stackelberg solution set-up is applied to obtain the equilibria of the proposed framework. Simulation results on the modified IEEE 69-bus test system and IEEE 123-bus test system reveal the validity of the proposed method

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server

Analysis of genetic diversity and construction of core collection of local mulberry varieties from Shanxi Province based on ISSR marker

Genetic diversity of 73 local mulberry varieties from Shanxi Province were screened using ISSR markers, with l5 primers combinations selected for their reproducibility and polymorphism. 129 bands were amplified, of which 115 bands showed polymorphism and the ratio of polymorphism bands was 89.15%. Nei’s genetic similarity coefficients ranged from 0.5891 to 0.9457 with an average of 0.7674. The observed number of alleles of each loci, effective number of alleles of each loci, Nei’s gene diversity, Shannon’s information index were 1.8915, 1.4771, 0.2780 and 0.4197, respectively. Clustering results showed that the 73 varieties could be divided into three different groups and nine subgroups. By using stepwise clustering and random methods and the modified heuristic algorithm, 21 core collections were constructed and the ratio of core collection was 28.77%. The result of t-test to the parameters (the number effective of alleles, Nei's genetic diversity index and Shannon's information index) showed that there was not significant difference between the core collection and initial sample with the exception of the number of observed alleles, that is, the core collection could well represent the initial sample.Key words: Mulberry, germplasm resource, genetic diversity, ISSR, cluster analysis, core collection