Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification.

The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy.

Identification of Gas Channeling and Construction of a Gel-Enhanced Foam Plugging System for Oxygen-Reduced Air Flooding in the Changqing Oilfield.

The accurate identification of gas channeling channels during foam-assisted oxygen-reduced air flooding (FAORAF) and the analysis of the main controlling factors are essential to propose reasonable and effective countermeasures to enhance oil recovery (EOR). However, there are few comprehensive studies on identifying gas channeling channels, the influencing factors, and the corresponding plugging EOR systems in FAORAF. The channeling channels of the injection and production wells of the Changqing Oilfield, China, under varying development schemes are identified utilizing fuzzy membership function theory in this work to obtain their primary distribution. The characteristics and influence factors of gas channeling channels are analyzed by numerical simulation using CMG. The recovery performance of each foam blocking system is evaluated by twin-tube sand pack models. As well, based on the features of reservoir fractures, a new gel-enhanced foam plugging system is developed. The results show that channeling channels chiefly develop along NE 60-70° and that foam could reduce gas channeling. Natural and artificial fractures are the principal factors causing gas channeling, followed by the injection method and gas injection rate. Under the premise of the injection and migration efficiency, the optimal gel system is a 0.1% HPAM + 0.1% organic chromium crosslinking agent. The addition of gel increases the viscosity of the liquid phase and strengthens the mechanical strength of the foam liquid film. At a permeability ratio of 12, the recovery factors of the binary plugging systems composed of microspheres, PEG, and gel combined with foam are 40.89%, 45.85%, and 53.33%, respectively. The movable gel foam system has a short breaking time (only 18 days) and a recovery factor of about 40% at a permeability ratio of 20. To be suitable for oil reservoirs with microfractures, an improved ternary gel foam system-0.1% HPAM + 0.1% chromium crosslinking agent + 0.05-0.1% nano-SiO2-is developed. Compared with the binary gel foam system, the recovery rate of the new nano-SiO2 gel foam system after 15 days of ageing using the core splitting test is 25.24% during the FAORAF process, increasing by 12.38%.

Performance Improvement of Handheld Raman Spectrometer for Mixture Components Identification Using Fuzzy Membership and Sparse Non-Negative Least Squares.

Due to the advantages of low price and convenience for end-users to conduct field-based, in-situ analysis, handheld Raman spectrometers are widely used in the identification of mixture components. However, the spectra collected by handheld Raman spectrometer usually have serious peak overlapping and spectral distortion, resulting in difficulties in component identification in the mixture. A novel method for mixture components identification based on the handheld Raman spectrometer was proposed in this study. The wavelet transform and Voight curve fitting method were used to extract the feature parameters from each Raman spectral peak, including Raman shift, maximum intensity, and full width at half-maximum (FWHM), and the similarities between the mixture and each substance in the database were calculated by fuzzy membership function based on extracted feature parameters. Then, the possible substances in the mixture were preliminarily screened out as candidates according to the similarity. Finally, the Raman spectra of these candidates were used to fit the spectra of the mixture, and the fitting coefficients obtained by sparse non-negative least squares algorithm were employed to further determine the suspected substance in the mixture. The Raman spectra of 190 liquid mixture samples and 158 powder mixture samples were collected using a handheld Raman spectrometer and these spectra were used to validate the identification performance of the proposed method. The proposed method could achieve good identification accuracy for different mixture samples. It shows that the proposed method is an effective way for the component identification in mixture by using a handheld Raman spectrometer.

Distributed and multicore QuBiLS-MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra.

Advances to the distributed, multi-core and fully cross-platform QuBiLS-MIDAS software v2.0 (http://tomocomd.com/qubils-midas) are reported in this article since the v1.0 release. The QuBiLS-MIDAS software is the only one that computes atom-pair and alignment-free geometrical MDs (3D-MDs) from several distance metrics other than the Euclidean distance, as well as alignment-free 3D-MDs that codify structural information regarding the relations among three and four atoms of a molecule. The most recent features added to the QuBiLS-MIDAS software v2.0 are related (a) to the calculation of atomic weightings from indices based on the vertex-degree invariant (e.g., Alikhanidi index); (b) to consider central chirality during the molecular encoding; (c) to use measures based on clustering methods and statistical functions to codify structural information among more than two atoms; (d) to the use of a novel method based on fuzzy membership functions to spherically truncate inter-atomic relations; and (e) to the use of weighted and fuzzy aggregation operators to compute global 3D-MDs according to the importance and/or interrelation of the atoms of a molecule during the molecular encoding. Moreover, a novel module to compute QuBiLS-MIDAS 3D-MDs from their headings was also developed. This module can be used either by the graphical user interface or by means of the software library. By using the library, both the predictive models built with the QuBiLS-MIDAS 3D-MDs and the QuBiLS-MIDAS 3D-MDs calculation can be embedded in other tools. A set of predefined QuBiLS-MIDAS 3D-MDs with high information content and low redundancy on a set comprised of 20,469 compounds is also provided to be employed in further cheminformatics tasks. This set of predefined 3D-MDs evidenced better performance than all the universe of Dragon (v5.5) and PaDEL 0D-to-3D MDs in variability studies, whereas a linear independence study proved that these QuBiLS-MIDAS 3D-MDs codify chemical information orthogonal to the Dragon 0D-to-3D MDs. This set of predefined 3D-MDs would be periodically updated as long as new results be achieved. In general, this report highlights our continued efforts to provide a better tool for a most suitable characterization of compounds, and in this way, to contribute to obtaining better outcomes in future applications.

Effects of one haustorium-inducing quinone DMBQ on growth and development of root hemiparasitic plant Monochasma savatieri / Efeitos de quinona indutor de haustório (DMBQ) no crescimento e desenvolvimento de plantas hemiparasitárias de raiz Monochasma savatieri

ABSTRACT: Monochasma savatieri Franch. ex Maxim is a perennial, parasitic herb used in traditional Chinese medicine and its wild resources have decreased sharply in recent years due to destructively harvesting and habitat destruction. Haustorium formation is a key event of parasites, but the concentrations of haustorium-inducing factors vary with species and cultivation conditions. In this study, we investigated the effects of the 2,6-dimethoxy-p-benzoquinone (DMBQ) concentration and cultivation density on the growth traits, haustorium formation and biomass of M. savatieri in the absence of a host plant. The results showed that both the DMBQ concentration and cultivation density regulated growth traits, haustorium formation and biomass in M. savatieri. The number of haustoria was significantly positively correlated with seedling height, maximum root length, the number of root tips and total dry weight. Membership function analysis revealed an overall greater increase in growth traits, haustorium formation and biomass when M. savatieri was treated with 10 μmol·L-1DMBQ and grew solitarily. These results offer an understanding of growth in M. savatieri influenced by the DMBQ concentration and cultivation density, which may aid in the establishment of a comprehensive cultivation system for M. savatieri or similar plants.

RESUMO: Monochasma savatieri Franch. O ex Maxim é uma erva parasitária aperene usada na medicina tradicional chinesa suas fontes diminuíram acentuadamente nos últimos anos devido à colheita destrutiva e à destruição de habitats e condições de sobrevivência no campo. Neste estudo, investigamos os efeitos da concentração de 2,6-dimetoxi-p-benzoquinona (DMBQ) e densidade de cultivo sobre as características de crescimento, formação de haustório e biomassa de M. savatieri na ausência de uma planta hospedeira. Os resultados mostraram que a concentração de DMBQ e a densidade de cultivo regularam as características de crescimento, a formação de haustório e a biomassa em M. savatieri. O número de haustórios foi significativamente correlacionado positivamente com a altura das plântulas, comprimento máximo das raízes, número de pontas das raízes e peso seco total. Revelou também um aumento geral nas características de crescimento, formação de haustório e biomassa quando M. savatieri foi tratado com 10 μmol • L-1DMBQ e cresceu solitariamente. Esses resultados oferecem uma compreensão do crescimento de M. savatieri influenciado pela concentração de DMBQ e densidade de cultivo, o que pode ajudar no estabelecimento de um sistema abrangente de cultivo para plantas similares de M. savatierior.

Classification of hydrocolloids based on small amplitude oscillatory shear, large amplitude oscillatory shear, and textural properties.

Dynamic rheological and mechanical properties of seven commercial (xanthan [XG], guar [GG], high methoxylated pectin [HMP], κ-carrageenan [κ-Car], agar [AG], alginate [ALG], and carboxymethylcellulose [CMC]) and four emerging hydrocolloids (basil seed gum [BSG], sage seed gum [SSG], Balangu-Shirazi seed gum [BSSG], and cress seed gum [CSG]) were investigated and the classification of the hydrocolloids were carried out based on them. AG belonged to the first class with 0.81 membership function (MF), κ-Car and HMP grouped in the second class with 0.68 and 0.71 MFs, respectively, XG, BSG, and SSG were depended to the third class with 0.61-0.70 MFs, finally, CMC, GG, BSSG, ALG, and CSG related to the fourth class, as the most populated class, with MF > 0.61. The first class contained the highest amount of hardness parameter (43.40 ± 2.76 g), the second class included the highest pseudoplasticity parameter (shear-thinning ratio = -0.54 ± 0.03) and relaxation time (66.25 ± 2.61 s) and the fourth cluster comprised the highest frequency dependency of viscous modulus (exponent of power-law model for viscous modulus vs. frequency = 0.30 ± 0.05). In addition, the results of this study showed that there was a distinct relationship between nonlinear harmonics in the stress wave and fundamental characteristics of hydrogel networks. The investigation of the rheo-mechanical properties of biopolymers in large deformation under shear and normal forces can have an important role in the prediction of the behavior of the material in real processes and application conditions, especially in the food industry. Due to the inconvenience of large deformation mechanical tests, such as Weissenberg effect, the complication of the results analyzing and sampling difficulty of semi-dilute samples; herein, we determined the correlation between large deformation (LAOS and texture analysis) and small deformation (SAOS) tests properties. The studied rheo-mechanical parameters showed high correlation with the four mentioned network parameters (more than 65% similarity index). Using these results, other scientists could rationally design the experiments and avoid experiments with similar parameters.

Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs.

In wireless sensor networks, clustering routing algorithms have been widely used owing to their high energy-efficiency and scalability. In clustering schemes, the nodes are organized in the form of clusters, and each cluster is governed by a cluster head. Once the cluster heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station using minimum energy (cost) routing. This approach significantly improves the network lifetime. Therefore, a new cluster head selection method that uses a weighted sum method to calculate the weight of each node in the cluster and compare it with the standard weight of that particular cluster is proposed in this paper. The node with a weight closest to the standard cluster weight becomes the cluster head. This technique balances the load distribution and selects the nodes with highest residual energy in the network. Additionally, a data routing scheme is proposed to determine an energy-efficient path from the source to the destination node. This algorithm assigns a weight function to each link on the basis of a fuzzy membership function and intra-cluster communication cost within a cluster. As a result, a minimum weight path is selected using Dijkstra's algorithm that improves the energy efficiency of the overall system. The experimental results show that the proposed algorithm shows better performance than some existing representative methods in the aspects of energy consumption, network lifetime, and system throughput.

Reliability analysis of a phaser measurement unit using a generalized fuzzy lambda-tau(GFLT) technique.

Nowadays power consumption is increasing day-by-day. To fulfill failure free power requirement, planning and implementation of an effective and reliable power management system is essential. Phasor measurement unit(PMU) is one of the key device in wide area measurement and control systems. The reliable performance of PMU assures failure free power supply for any power system. So, the purpose of the present study is to analyse the reliability of a PMU used for controllability and observability of power systems utilizing available uncertain data. In this paper, a generalized fuzzy lambda-tau (GFLT) technique has been proposed for this purpose. In GFLT, system components' uncertain failure and repair rates are fuzzified using fuzzy numbers having different shapes such as triangular, normal, cauchy, sharp gamma and trapezoidal. To select a suitable fuzzy number for quantifying data uncertainty, system experts' opinion have been considered. The GFLT technique applies fault tree, lambda-tau method, fuzzified data using different membership functions, alpha-cut based fuzzy arithmetic operations to compute some important reliability indices. Furthermore, in this study ranking of critical components of the system using RAM-Index and sensitivity analysis have also been performed. The developed technique may be helpful to improve system performance significantly and can be applied to analyse fuzzy reliability of other engineering systems.

FuzzyID2: A software package for large data set species identification via barcoding and metabarcoding using hidden Markov models and fuzzy set methods.

Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two-step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA-based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%-100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity-related projects with large DNA sequence data sets.

Genetic algorithm based hybrid approach to solve fuzzy multi-objective assignment problem using exponential membership function.

This paper presents a genetic algorithm based hybrid approach for solving a fuzzy multi-objective assignment problem (FMOAP) by using an exponential membership function in which the coefficient of the objective function is described by a triangular possibility distribution. Moreover, in this study, fuzzy judgment was classified using α-level sets for the decision maker (DM) to simultaneously optimize the optimistic, most likely, and pessimistic scenarios of fuzzy objective functions. To demonstrate the effectiveness of the proposed approach, a numerical example is provided with a data set from a realistic situation. This paper concludes that the developed hybrid approach can manage FMOAP efficiently and effectively with an effective output to enable the DM to take a decision.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Predictions of potential geographical distribution and quality of Schisandra sphenanthera under climate change.

Climate change will significantly affect plant distribution as well as the quality of medicinal plants. Although numerous studies have analyzed the effect of climate change on future habitats of plants through species distribution models (SDMs), few of them have incorporated the change of effective content of medicinal plants. Schisandra sphenanthera Rehd. et Wils. is an endangered traditional Chinese medical plant which is mainly located in the Qinling Mountains. Combining fuzzy theory and a maximum entropy model, we obtained current spatial distribution of quality assessment for S. spenanthera. Moreover, the future quality and distribution of S. spenanthera were also projected for the periods 2020s, 2050s and 2080s under three different climate change scenarios (SRES-A1B, SRES-A2 and SRES-B1 emission scenarios) described in the Special Report on Emissions Scenarios (SRES) of IPCC (Intergovernmental Panel on Climate Change). The results showed that the moderately suitable habitat of S. sphenanthera under all climate change scenarios remained relatively stable in the study area. The highly suitable habitat of S. sphenanthera would gradually decrease in the future and a higher decline rate of the highly suitable habitat area would occur under climate change scenarios SRES-A1B and SRES-A2. The result suggested that in the study area, there would be no more highly suitable habitat areas for S. sphenanthera when the annual mean temperature exceeds 20 °C or its annual precipitation exceeds 1,200 mm. Our results will be influential in the future ecological conservation and management of S. sphenanthera and can be taken as a reference for habitat suitability assessment research for other medicinal plants.