Best Set Of Values Research Articles

Intelligent Deep Adaptive Intuitionistic Fuzzy Classifier Based Dyslexia Prediction Among Children at its Early Stage

The inability of standard linear research to clearly define the role of cognitive abilities in reading difficulties is most likely due to the fact that reading and the components that are related to reading are both part of a system of factors. There are numerous interrelated and mitigating elements that conventional statistical models are unable to deal effectively. This work aims to increase dyslexia prediction accuracy by introducing two stage non-linear methodology which incorporate both deep learning and uncertainty theory. In the first stage, the pattern of dyslexic symptoms is learned by introducing deep adaptive neural network. During the training phase, the hyperparameters weight and learning rate are optimized using a metaheuristic algorithm called the fruit fly optimization algorithm. This algorithm imitates the behavior of fruit flies to search for the best set of values. The acquired knowledge is infused in the uncertainty inference model known as intuitionistic inference classifier. Each characteristic that defines an instance as dyslexic or non-dyslexic is represented in tristate degrees to precisely address the inconsistent or vague instance which neither or nor exhibit dyslexic symptoms by introducing the hesitancy factor. The simulation results support the proposed intuitionistic fuzzy rules prominently improves the accuracy rate of dyslexia prediction compared to the other existing state of arts.

A new tool to derive simultaneously exponent and extremes of power-law distributions

ABSTRACT Many experimental quantities show a power-law distribution p(x) ∝ x−α. In astrophysics, examples are: size distribution of dust grains or luminosity function of galaxies. Such distributions are characterized by the exponent α and by the extremes xminxmax where the distribution extends. There are no mathematical tools that derive the three unknowns at the same time. In general, one estimates a set of α corresponding to different guesses of xminxmax. Then, the best set of values describing the observed data is selected a posteriori. In this paper, we present a tool that finds contextually the three parameters based on simple assumptions on how the observed values xi populate the unknown range between xmin and xmax for a given α. Our tool, freely downloadable, finds the best values through a non-linear least-squares fit. We compare our technique with the maximum likelihood estimators for power-law distributions, both truncated and not. Through simulated data, we show for each method the reliability of the computed parameters as a function of the number N of data in the sample. We then apply our method to observed data to derive: (i) the slope of the core mass function in the Perseus star-forming region, finding two power-law distributions: α = 2.576 between $1.06\, \mathrm{M}_{\odot }$ and $3.35\, \mathrm{M}_{\odot }$, α = 3.39 between $3.48\, \mathrm{M}_{\odot }$ and $33.4\, \mathrm{M}_{\odot }$; (ii) the slope of the γ-ray spectrum of the blazar J0011.4+0057, extracted from the Fermi-LAT archive. For the latter case, we derive α = 2.89 between 1484 MeV and 28.7 GeV; then we derive the time-resolved slopes using subsets of 200 photons each.

Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight

Smart home is a concept that aims to maximize the comfort of occupant while consuming energy as low as possible. The comfort and energy consumption are contradicting factors in smart homes. Enhancing comfort often requires considerable energy. On the other hand, minimizing energy may result in less comfort to the residence. Thus, maximizing comfort while minimizing energy consumption is a challenging process. In this paper, bat algorithm (BA) based solution is proposed to tackle this problem. Three main parameters that influence the occupant’s comfort, namely, temperature, illumination, and indoor air quality, are considered. The algorithm optimizes towards the best set of values for the three parameters. In this work, exponentially increasing inertia weight is introduced to BA for performance improvement. A secondary dataset consisting of 48 environmental values is optimized using the proposed algorithm. The performance of BA with exponential inertia weight is proven as significantly better than the original BA and other variants of inertia weight; random, linearly decreasing, and nonlinearly decreasing. Moreover, the comfort level achieved by BA with exponential inertia weight is found to be better than previously reported works using firefly algorithm, genetic algorithm, ant colony optimization, and artificial bee colony algorithm. The superior performance is achieved due to better convergence behaviour. Summarily, applying BA with exponential inertia weight is a novel contribution that is essential for providing smart home system a solution that ensures optimum comfort at minimal energy consumption.

An effective dynamic immune optimization control for the wastewater treatment process.

To resolve the conflict between multiple performance indicators in the complicated wastewater treatment process (WWTP), an effective optimization control scheme based on a dynamic multi-objective immune system (DMOIA-OC) is designed. A dynamic optimization control scheme is first developed in which the control process is divided into a dynamic layer and a tracking control layer. Based on the analysis of the WWTP performance, the energy consumption and effluent quality models are next established adaptively in response to the environment by an optimization layer. An adaptive dynamic immune optimization algorithm is then proposed to optimize the complex and conflicting performance indicators. In addition, a suitable preferred solution is selected from the numerous Pareto solutions to obtain the best set of values for the dissolved oxygen and nitrate nitrogen. Finally, the solution is evaluated on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem for multiple performance indicators in WWTPs and has a competitive advantage in its control effect.

Cubic Nanoparticles for Magnetic Hyperthermia: Process Optimization and Potential Industrial Implementation.

Cubic nanoparticles are referred to as the best shaped particles for magnetic hyperthermia applications. In this work, the best set of values for obtaining optimized shape and size of magnetic particles (namely: reagents quantities and proportions, type of solvents, temperature, etc.) is determined. A full industrial implementation study is also performed, including production system design and technical and economic viability.

Development of statistical estimators for speech enhancement using multi-objective grey wolf optimizer

Statistical Estimation using the SNR uncertainty technique is one of the effective Speech Enhancement (SE) algorithms. In this method, the Gain function plays a crucial role and it depends on the proper selection of the smoothing and threshold constants. In the literature, the values of these constants have been optimized by considering a single objective function of maximization of speech quality for a specific noise condition. But in practice, the noise magnitude varies and one set of optimized parameters cannot always provide consistent performance. In this paper, this problem has been addressed and solved in three steps. The first step is multi-objective optimization to find the best set of values of smoothing and threshold constants at different noise levels by considering the objectives of maximization of speech quality, intelligibility, and minimization of mean square error. The second step is the classification of the noisy speech into four SNR levels such as 0 dB, 5 dB, 10 dB, and 15 dB by using appropriate audio features. The values obtained in steps one and two are stored and in the third step, when the unknown noisy speech signal is to be enhanced the best-chosen values of the smoothing and threshold constants are selected for this task. Finally, the performance of the proposed method is evaluated in two different speech datasets. Then, comparative performance and statistical analysis are carried out using six other standard SE algorithms and it is demonstrated that the proposed approach provides superior performance than others.

Water Decontamination with Magnetic Particles by Adsorption and Chemical Degradation. Influence of the Manufacturing Parameters.

Many different processes for manufacturing of magnetic particles are present in scientific literature. However, the large majority are not able to be applied to large-scale real operations. In this study, we present an experiment undertaken to determine advisable values and options for the main variables and factors for the application of the reverse co-precipitation method to produce magnetic particles for real environmental applications. In such, we have tried a conjugation of values/factors that has led to 12 main experiments and production of 12 different particles. After an initial study concerning their main characteristics, these 12 different particles were applied for the sorption removal of COD from real wastewater samples (efficiencies between 70% and 81%) and degradation of Methylene blue by Fenton reaction (degradation efficiencies up to 100%). The main conclusion from this work is that the best set of values depends on the target environmental application, and this set of values were determined for the two applications studied.

TrackEtching — A Java based code for etched track profile calculations in SSNTDs

A java code incorporating a user friendly GUI has been developed to calculate the parameters of chemically etched track profiles of ion-irradiated solid state nuclear track detectors. Huygen’s construction of wavefronts based on secondary wavelets has been used to numerically calculate the etched track profile as a function of the etching time. Provision for normal incidence and oblique incidence on the detector surface has been incorporated. Results in typical cases are presented and compared with experimental data. Different expressions for the variation of track etch rate as a function of the ion energy have been utilized. The best set of values of the parameters in the expressions can be obtained by comparing with available experimental data. Critical angle for track development can also be calculated using the present code. Program summaryProgram Title: TrackEtchingProgram Files doi:http://dx.doi.org/10.17632/7wb6pvpbgh.1Licensing provisions: CC0 1.0Programming language: JavaNature of problem: Calculation of the etched track profiles in ion irradiated SSNTDs is important for the application in the field of nucleopore filters and for their use as templates for nano wire fabrication.Solution method: A numerical approach, based on Huygen’s method for optical wavefront calculations has been employed. At any etching time, the etching is assumed to proceed along the normal to the etched surface. Several analytical functions are tried for the variation of the track etch velocity along the ion track as a function of the depth inside the surface and the energy/stopping power of the ion in the medium.Additional comments including Restrictions and Unusual features: The program does not generate three-dimensional profile. Provision for double sided etching will be incorporated in a later version of the code.

The vertical distribution of the Venus NO nightglow: Limb profiles inversion and one-dimensional modeling

Ultraviolet (UV) spectra of the δ (190–240nm) and γ (225–270nm) bands of the nitric oxide (NO) molecule have been measured on the nightside of the atmosphere of Venus with the Spectroscopy for Investigation of Characteristics of the Atmosphere of Venus (SPICAV) instrument on board Venus Express (VEX). Excited NO molecules on the nightside of the planet are created by radiative recombination of O(3P) and N(4S) atoms. The atoms are produced by photodissociation of CO2 and N2 molecules on the dayside and then transported on the nightside by the global circulation. We analyze all nightside limb profiles obtained since 2006 and provide a statistical study of the nitric oxide airglow layer and its variability. We also apply a spatial deconvolution and an Abel inversion method to the limb profiles to retrieve and quantify the volume emission rate distribution and its dependence on several factors. We also show that about 10% of the limb profiles exhibits a secondary peak located above or below the main airglow peak. Furthermore, a one-dimensional chemical-diffusive model is used to simultaneously model the globally averaged NO and O2(a1Δg) airglow vertical distributions using CO2 and O density profiles rooted in VIRTIS and SPICAV observations. We find that a downward flux of 2×109 N(4S) atoms cm−2s−1 and a eddy diffusion coefficient equal to 1×1011/ncm−2s−1, where n is the total number density, provide the best set of values to parametrize the one-dimensional representation of the complex 3-D dynamical processes.

Ontological content‐based filtering for personalised newspapers

PurposeThe purpose of this paper is to describe a new ontological content‐based filtering method for ranking the relevance of items for readers of news items, and its evaluation. The method has been implemented in ePaper, a personalised electronic newspaper prototype system. The method utilises a hierarchical ontology of news; it considers common and related concepts appearing in a user's profile on the one hand, and in a news item's profile on the other hand, and measures the “hierarchical distances” between these concepts. On that basis it computes the similarity between item and user profiles and rank‐orders the news items according to their relevance to each user.Design/methodology/approachThe paper evaluates the performance of the filtering method in an experimental setting. Each participant read news items obtained from an electronic newspaper and rated their relevance. Independently, the filtering method is applied to the same items and generated, for each participant, a list of news items ranked according to relevance.FindingsThe results of the evaluations revealed that the filtering algorithm, which takes into consideration hierarchically related concepts, yielded significantly better results than a filtering method that takes only common concepts into consideration. The paper determined a best set of values (weights) of the hierarchical similarity parameters. It also found out that the quality of filtering improves as the number of items used for implicit updates of the profile increases, and that even with implicitly updated profiles, it is better to start with user‐defined profiles.Originality/valueThe proposed content‐based filtering method can be used for filtering not only news items but items from any domain, and not only with a three‐level hierarchical ontology but any‐level ontology, in any language.

The hybrid model of neural networks and genetic algorithms for the design of controls for internet-based systems for business-to-consumer electronic commerce

As organizations become increasingly dependent on Internet-based systems for business-to-consumer electronic commerce (ISB2C), the issue of IS security becomes increasingly important. As the usage of security controls is related to the implementation of ISB2C, the extent of ISB2C controls can be adjusted in order to enable the greatest extent of implementation of ISB2C. This study intends to propose ISB2C-NNGA (ISB2C-controls design using neural networks and genetic algorithms), a hybrid optimization model using neural networks and genetic algorithms for the design of ISB2C controls, which uses back-propagation neural networks (BPN) model as a prediction of controls using system environments, and GA as a pattern directed search mechanism to estimate the exponent of independent variables (i.e., ISB2C controls) in multivariate regression analysis of power model. The effect of system environments on controls can be estimated using BPN model which outperformed linear regression analysis in terms of square root of mean squared error. The effect of each mode of controls on implementation (volume) can be identified using exponents and standardized coefficients in the GA-based nonlinear regression analysis in ISB2C-NNGA. ISB2C-NNGA outperformed conventional linear regression analysis in prediction accuracy in terms of the average R square and sum of squared error. ISB2C can suggest the best set of values for controls to be recommended from several candidate sets of values for controls by identifying the set of values for controls which produce greatest extent of ISB2C implementation. The results of study will support the design of ISB2C controls effectively.

Rate constants for decarboxylation reactions calculated using no barrier theory

No barrier theory (NBT) provides both a qualitative way of thinking about what makes a reaction fast or slow and a quantitative way of calculating the rate constant (free energy of activation) corresponding to a particular mechanism. The origin and development of this idea are reviewed and examples of its use for qualitative understanding are presented before applying it to a set of decarboxylations. From the literature, a set of best values for rate constants for decarboxylation was picked. Detailed mechanistic models were developed for reactions leading to delocalized “anions” or to localized anions. It was necessary to have pKa values for ionizaion of the carbon acids corresponding to all of these species and these were selected from the literature or estimated by linear free energy relations (or occasionally calculated from proton exchange data). Over the entire range of measured decarboxylation rate constants, a range of 1025 in rate constant, the calculated values were in good agreement with experiment, with two exceptions: malonate dianion, which has been reported but probably not measured, and glycine, where it is possible that a different mechanism is being followed, unfortunately, one which we do not yet know how to treat by NBT. NBT is both a qualitatively and quantitatively useful tool for understanding chemistry.

The Positive Temperature Coefficient of Resistivity Effect in the Pb(Fe1/2Nb1/2)O3Ceramics Admixed with Lithium

In the paper technological conditions were looked for to obtain the Pb(Fe1/2Nb1/2)O3 (PFN) ceramics and the PFN ceramics with the lithium admixture with the highest electric conduction and conditions appropriate for the PTC-R effect formation. Measurements of dc electrical resistivity, dielectric permittivity and dielectric loss were made as functions of temperature from room temperature to 190◦C, at 1 kHz. The best set of values of dielectric loss and dielectric constant, from the ferroelectricity point of view, were obtained when the precursor with orthorhombic structure was employed. By creating appropriate technological conditions in the PFN ceramics the PTC-R effect can occur above the Curie temperature. Their electric properties are determined by presence of a basic phase of the potential barrier on the grain boundaries.

The hydraulic separator Multidune: Preliminary tests on fluid-dynamic features and plastic separation feasibility

Recycling of plastic materials is a rapidly developing discipline because of environmental awareness, the need to conserve materials and energy, and the growing demand to increase the production economy. The main problem in plastics recovery and recycling is related to the variety of plastic wastes, even if selective collection occurs. Therefore, plastic materials can be recycled either as mixtures or as single types, separating the different typologies by their physical (size, specific mass, etc.) and/or chemical properties. However, separation of plastics in single typologies by traditional processes and devices is difficult due to their typical low variability in properties. This paper presents a new research development for recycling industry: the Multidune separator. This is a device constructed from a sequence of parallel semi-cylindrical tubes of transparent plastic welded together in a plane. The lower half is shifted laterally and then fixed relative to the upper half. Flow is then induced in the lateral direction normal to the axis of the tubes, creating a main flow channel and two recirculation zones. This apparatus creates a differential transport of particles of low specific mass, near to 1g/cm3, allowing their separation. The flow field in the Multidune separator is studied via Particle Tracking Velocimetry (PTV). Eulerian analysis of the data is performed to gather information about the fluid-dynamics features established by different hydraulic heads at the inlet of the Multidune. Preliminary tests on monomaterial samples have been performed, varying several operative parameters to determine the best set of values. Therefore, separation tests have been executed on composite samples, obtaining satisfactory results in terms of plastic separation feasibility.

Frequency-temperature response of ferroelectromagnetic Pb(Fe1∕2Nb1∕2)O3 ceramics obtained by different precursors. Part I. Structural and thermo-electrical characterization

With the purpose of fabricating ceramics where ferroelectric and magnetic order coexist, ceramics of Pb(Fe1∕2Nb1∕2)O3 have been prepared using the traditional ceramic method following three different routes. The first is a direct via starting from oxide reagents and the other two use different kinds of FeNbO4 precursors with either monoclinic or orthorhombic structures. Crystallographic and surface morphological studies were carried out by the powder x-ray diffraction and scanning microscopy techniques. The presence of Fe2+, detrimental to the ferroelectric and magnetic performance, was evaluated by x-ray photoelectron spectroscopy. The samples showed no structural differences, uniformly distributed grains, a ferro-paraelectric transition temperature at 110°C and a normal diffuse phase transition (nonrelaxor behavior). Differences in the degree of diffuseness, densities and grain size were observed depending on the kind of precursor. Measurements of dc and ac electrical resistivity, dielectric constant and dielectric loss were made as functions of temperature from room temperature to 250°C, at different frequency values (between 20Hz and 1MHz). Four conduction mechanisms were identified: hopping charge corresponding to low temperatures, small polarons and oxygen vacancies conduction at intermediate temperatures, and intrinsic ionic conduction at high temperatures. The best set of values of dielectric loss and dielectric constant, from the ferroelectricity point of view, were obtained when the precursor with orthorhombic structure was employed.

Managing quality in continuous casting process using product quality model and simulated annealing

This paper proposes an approach using quality loss functions and multicriteria optimization procedure to determine appropriate process parameter values for producing quality products in a continuous casting system. Quality of a continuously cast product depends on the process conditions during the casting process. From the information available in the literature and industry, 17 critical quality conditions have been identified which have to be satisfied to prevent defect formation during casting. Each of these process conditions are connected to one or more than one process parameters and the process parameter values are to be selected such that all these 17 quality conditions are satisfied simultaneously. The optimization procedure suggested in the present study involves representing the relationship between the responses and the process variables in the form of an objective function and finding the best set of values for the process variables. The objective function is formulated as a loss function, which establishes a functional relationship between the input variables or process parameters and the quality criteria. The optimum values of the process parameters were obtained by minimizing the loss function using simulated annealing optimization algorithm. The details are presented in this paper with the help of a specific case.

A method to defuzzify the fuzzy number: transportation problem application

In many problems of transportation engineering and planning, we encounter situations, in which the observed or derived values of the variables are approximate, yet the variables themselves must satisfy a set of rigid relationships dictated by physical principle. When the observed values do not satisfy the relationships, each value is adjusted until they satisfy the relationship. We propose a simple adjustment method that finds the most appropriate set of crisp numbers. The method assumes that each observed value is an approximate number (or a fuzzy number) and the true value is found in the support of the membership function. For each of many possible sets of values that satisfy the relationships, the lowest membership grade is checked and the set whose lowest membership grade is the highest is chosen as the best set of values for the problem. This process is performed using the fuzzy linear programming method. The paper presents the model, the computational process and applications.

Study of the effects of weathering on the chemical composition of a light crude oil using GC/MS GC/FID

AbstractQuantitative information on the weathering of spilled oil is essential to a fuller understanding of the fate and behavior of oil in the environment. Such data is also useful for spill modeling. The key to acquiring data on oil weathering is the availability of precise and reliable chemical information. Exact quantitation of compounds in the oil can provide this crucial data. In this study, the effects of weathering on the chemical composition of a light crude oil, Alberta Sweet Mix Blend (ASMB), were thoroughly investigated using GC/FID and GC/MS. Complete compositional information on the ASMB oil at various degrees of evaporation (0–45%) was obtained, and the composition and concentration changes of key components and component groupings were quantitatively correlated to evaporative loss. Two opposing effects during evaporation—one is the loss of oil components due to evaporation, and another is build‐up of oil components due to volume deduction—were examined. So‐called “pattern recognition” plots involving more than 100 important individual oil components and component groupings were graphically depicted, and these permitted deduction of a best set of values for quantitation of exposure to evaporative weathering. A “weathering index” concept is proposed. Relatively simple and very useful mathematical equations were derived which can be utilized to describe the weathering behavior of oil and to estimate the evaporation extent of oil. © 1995 John Wiley & Sons, Inc.

CHEMICAL ANALYSIS METHODS FOR CRUDE OIL

ABSTRACT Using an oil chemical analysis method developed in our laboratory, more than 280 compounds in the Alberta Sweet Mixed Blend (ASMB) oil have been positively identified. The distribution of selected target compounds offers unique and sensitive fingerprints for matching the source of spilled oil and tracing the weathering process of oil in the environment. The pattern recognition plots involving more than 100 oil-specific individual components and component groupings permit deduction of a best set of values for oil exposure to weathering. Using data from different samples including crude oil, weathered oil, and 22-year-old spilled oil as examples, the distinct advantages of the described method over the current methods are examined.

Determining the range of predictions of a groundwater model which arises from alternative calibrations

A major element in constructing a groundwater model is choosing the parameter values. The traditional approach is to aim for a single best set of values. The parameters used in a model are effective rather than measurable, and this combined with the inherent uncertainties in the modeling process means that there are often many plausible sets of values. A single prediction obtained from a single set of parameter values is not appropriate, but rather the range in predictions from the alternative calibrations should be used. A method is presented for finding the best case and worst case predictions among the plausible parameter sets and is applied to a real case study. Widely different feasible parameter sets were found giving significantly different predictions.