Selection of dialysis methods for end-stage kidney disease patients with diabetes

The increasing prevalence of diabetes has led to a growing population of endstage kidney disease(ESKD)patients with diabetes.Currently,kidney transplantation is the best treatment option for ESKD patients;however,it is limited by the lack of donors.Therefore,dialysis has become the standard treatment for ESKD patients.However,the optimal dialysis method for diabetic ESKD patients remains controversial.ESKD patients with diabetes often present with complex conditions and numerous complications.Furthermore,these patients face a high risk of infection and technical failure,are more susceptible to malnutrition,have difficulty establishing vascular access,and experience more frequent blood sugar fluctuations than the general population.Therefore,this article reviews nine critical aspects:Survival rate,glucose metabolism disorder,infectious complications,cardiovascular events,residual renal function,quality of life,economic benefits,malnutrition,and volume load.This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis,thereby improving patients’quality of life and survival rates.

Comparative Analysis of Improving Effects of 12 Maize Populations with Improved S_1 Selection Method

ln this study, 12 maize populations were improved with the improved S1 selection method, aiming to increase the improvement efficiency of maize popula-tions. The results showed that number of rows per spike, number of grains per row and 100-grain weight were the three important component traits of maize yield. The highest genetic increment was found in Mengqun 2, fol owed by Mengqun 4, Mengqun 1 and other 7 maize populations. Negative genetic increment was shown in Mengqun 3 and 3 introduced foreign maize populations. Some changes were shown in spikes, plant traits and genetic diversity of maize populations. Based on our results, we concluded that Mengqun 2, Mengqun 4, Zhongzong 7, Mengqun C and Mengqun A could be directly used for the line breeding by selfing for their higher genetic increment of yield and better improvement effects of other agricultural traits. Compared with these 5 populations, the improvement potential of other maize populations was limited for their lower genetic increment.

A personalized trustworthy service selection method

A personalized trustworthy service selection method is proposed to fully express the features of trust, emphasize the importance of user preference and improve the trustworthiness of service selection. The trustworthiness of web service is defined as customized multi-dimensional trust metrics and the user preference is embodied in the weight of each trust metric. A service selection method combining AHP （analytic hierarchy process） and PROMETHEE （preference ranking organization method for enrichment evaluations） is proposed. AHP is used to determine the weights of trust metrics according to users＇ preferences. Hierarchy and pairwise comparison matrices are constructed. The weights of trust metrics are derived from the highest eigenvalue and eigenvector of the matrix. to obtain the final rank of candidate services. The preference functions are defined according to the inherent characteristics of the trust metrics and net outranking flows are calculated. Experimental results show that the proposed method can effectively express users＇ personalized preferences for trust metrics, and the trustworthiness of service ranking and selection is efficiently improved.

Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV

To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible.

Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method

One of the most critical and complicated steps in mine design is a selection of suitable mining method based upon geological,geotechnical,geographical,safety and economical parameters.The aim of this study is developing a Monte Carlo simulation to selection the optimum mining method by using effective and major criteria and at the same time,taking subjective judgments of decision makers into consideration.Proposed approach is based on the combination of Monte Carlo simulation with conventional Analytic Hierarchy Process(AHP).Monte Carlo simulation is used to determine the confdence level of each alternative’s score,is calculated by AHP,with the respect to the variance of decision makers’opinion.The proposed method is applied for Jajarm Bauxite Mine in Iran and eventually the most appropriate mining methods for this mine are ranked.

NEW FEATURE SELECTION METHOD IN MACHINE FAULT DIAGNOSIS

Aiming to deficiency of the filter and wrapper feature selection methods, anew method based on composite method of filter and wrapper method is proposed. First the methodfilters original features to form a feature subset which can meet classification correctness rate,then applies wrapper feature selection method select optimal feature subset. A successful techniquefor solving optimization problems is given by genetic algorithm (GA). GA is applied to the problemof optimal feature selection. The composite method saves computing time several times of the wrappermethod with holding the classification accuracy in data simulation and experiment on bearing faultfeature selection. So this method possesses excellent optimization property, can save more selectiontime, and has the characteristics of high accuracy and high efficiency.

Physical and numerical investigations of target stratum selection for ground hydraulic fracturing of multiple hard roofs

Ground hydraulic fracturing plays a crucial role in controlling the far-field hard roof,making it imperative to identify the most suitable target stratum for effective control.Physical experiments are conducted based on engineering properties to simulate the gradual collapse of the roof during longwall top coal caving(LTCC).A numerical model is established using the material point method(MPM)and the strain-softening damage constitutive model according to the structure of the physical model.Numerical simulations are conducted to analyze the LTCC process under different hard roofs for ground hydraulic fracturing.The results show that ground hydraulic fracturing releases the energy and stress of the target stratum,resulting in a substantial lag in the fracturing of the overburden before collapse occurs in the hydraulic fracturing stratum.Ground hydraulic fracturing of a low hard roof reduces the lag effect of hydraulic fractures,dissipates the energy consumed by the fracture of the hard roof,and reduces the abutment stress.Therefore,it is advisable to prioritize the selection of the lower hard roof as the target stratum.

Optimum mining method selection using fuzzy analytical hierarchy process–Qapiliq salt mine,Iran

Mining method selection is the first and the most critical problem in mine design and depends on some parameters such as geotechnical and geological features and economic and geographic factors. In this paper, the factors affecting mining method selection are determined. These factors include shape, thick- ness, depth, slope, RMR and RSS of the orebody, RMR and RSS of the hanging wall and footwall. Then, the priorities of these factors are calculated. In order to calculate the priorities of factors and select the best mining method for Qapiliq salt mine, Iran, based on these priorities, fuzzy analytical hierarchy process （AHP） technique is used. For this purpose, a questionnaire was prepared and was given to the associated experts. Finally, after a comparison carried out based on the effective factors, between the four mining methods including area mining, room and pillar, cut and fill and stope and pillar methods, the stope and nillar mining method was selected as the most suitable method to this mine.

Comparison of Block Design Nonparametric Subset Selection Rules Based on Alternative Scoring Rules

This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statistics of the uniform distribution (yielding rank values) and of the normal distribution (yielding normal score values). The comparison is made using state motor vehicle traffic fatality rates, published in a 2016 article, with fifty-one states (including DC as a state) and over a nineteen-year period (1994 through 2012). The earlier study considered four block design selection rules—two for choosing a subset to contain the “best” population (i.e., state with lowest mean fatality rate) and two for the “worst” population (i.e., highest mean rate) with a probability of correct selection chosen to be 0.90. Two selection rules based on normal scores resulted in selected subset sizes substantially smaller than corresponding rules based on ranks (7 vs. 16 and 3 vs. 12). For two other selection rules, the subsets chosen were very close in size (within one). A comparison is also made using state homicide rates, published in a 2022 article, with fifty states and covering eight years. The results are qualitatively the same as those obtained with the motor vehicle traffic fatality rates.

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

A PMU data recovering method based on preferred selection strategy

Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monitoring and close-loop control applications. However, the PMUs data quality issue affects applications based on PMUs a lot. This paper proposes a simple yet effective method for recovering PMU data. To simply the issue, two different scenarios of PMUs data loss are first defined. Then a key combination of preferred selection strategies is introduced. And the missing data is recovered by the function of spline interpolation. This method has been tested by artificial data and field data obtained from on-site PMUs. The results demonstrate that the proposed method recovers the missing PMU data quickly and accurately. And it is much better than other methods when missing data are massive and continuous. This paper also presents the interesting direction for future work.

Performance Comparison of Two Efficient Genomic Selection Methods(gsbay & MixP ) Applied in Aquacultural Organisms

Genomic selection is more and more popular in animal and plant breeding industries all around the world, as it can be applied early in life without impacting selection candidates. The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different genomic selection tools Mix P and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop(Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction(GBLUP) method which has been applied widely. Our results showed that both Mix P and gsbay could accurately estimate single-nucleotide polymorphism(SNP) marker effects, and thereby could be applied for the analysis of genomic estimated breeding values(GEBV). In simulated data from different scenarios, the accuracy of GEBV acquired was ranged from 0.20 to 0.78 by Mix P; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by Mix P and gsbay were expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with Mix P the computation is much faster, especially in dealing with large-scale data. These results suggested that both algorithms implemented by Mix P and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.

A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.

Finite Temperature Lanczos Method with the Stochastic State Selection and Its Application to Study of the Higgs Mode in the Antiferromagnet at Finite Temperature

We propose an improved finite temperature Lanczos method using the stochastic state selection method. In the finite temperature Lanczos method, we generate Lanczos states and calculate the eigenvalues. In addition we have to calculate matrix elements that are the values of an operator between two Lanczos states. In the calculations of the matrix elements we have to keep the set of Lanczos states on the computer memory. Therefore the memory limits the system size in the calculations. Here we propose an application of the stochastic state selection method in order to weaken this limitation. This method is to select some parts of basis states stochastically and to abandon other basis state. Only by the selected basis states we calculate the inner product. After making the statistical average, we can obtain the correct value of the inner product. By the stochastic state selection method we can reduce the number of the basis states for calculations. As a result we can relax the limitation on the computer memory. In order to study the Higgs mode at finite temperature, we calculate the dynamical correlations of the two spin operators in the spin-1/2 Heisenberg antiferromagnet on the square lattice using the improved finite temperature Lanczos method. Our results on the lattices of up to 32 sites show that the Higgs mode exists at low temperature and it disappears gradually when the temperature becomes large. At high temperature we do not find this mode in the dynamical correlations.

A novel adaptive harmonic balance method with an asymptotic harmonic selection

The harmonic balance method(HBM)is one of the most widely used methods in solving nonlinear vibration problems,and its accuracy and computational efficiency largely depend on the number of the harmonics selected.The adaptive harmonic balance(AHB)method is an improved HBM method.This paper presents a modified AHB method with the asymptotic harmonic selection(AHS)procedure.This new harmonic selection procedure selects harmonics from the frequency spectra of nonlinear terms instead of estimating the contribution of each harmonic to the whole nonlinear response,by which the additional calculation is avoided.A modified continuation method is proposed to deal with the variable size of nonlinear algebraic equations at different values of path parameters,and then all solution branches of the amplitude-frequency response are obtained.Numerical experiments are carried out to verify the performance of the AHB-AHS method.Five typical nonlinear dynamic equations with different types of nonlinearities and excitations are chosen as the illustrative examples.Compared with the classical HBM and Runge-Kutta methods,the proposed AHB-AHS method is of higher accuracy and better convergence.The AHB-AHS method proposed in this paper has the potential to investigate the nonlinear vibrations of complex high-dimensional nonlinear systems.

Secure Path Cycle Selection Method Using Fuzzy Logic System for Improving Energy Efficiency in Statistical En-Route Filtering Based WSNs

Sensor nodes are easily compromised to malicious attackers due to an open environment. A false injected attack which takes place on application layer is elected by the compromised node. If the false report arrives in a base station, a false alarm is occurred, and the energy of the nodes is consumed. To detect the false report, statistical en-route filtering method is proposed. In this paper, we proposed the secure path cycle selection method using fuzzy rule-based system to consume effective energy. The method makes balanced energy consumption of each node. Moreover, the lifetime of the whole network will be increased. The base station determines the path cycle using the fuzzy rule-based system. The performance of the proposed method is demonstrated using simulation studies with the three methods.

Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection

The paper describes the necessity of application of intelligent technologies to support decisions of more objective problems in human resource management. In this paper, we describe the methodology for personnel selection problem for the vacancy with regard to the importance and nonequivalence of numerous indicators characterizing the alternatives. The specific features of the selection problem are highlighted, immersing the problem into a fuzzy environment. A fuzzy multicriterial model of the personnel selection problem is proposed. A technique of order preference by similarity to ideal solition (TOPSIS), was applied for evaluation and regulation of alternatives. This technique is based on criteria of qualitative character, which are hierarchically structured by multiple experts to intellectually support decisions made in personnel selection problem. Using TOPSIS method and generated criteria system an experiment was conducted for evaluation of the candidates during solution of hiring problems. The obtained and reviewed results were compared with results obtained using in reality.

An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis

With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.

Similarity evaluation of stratum anti-drilling ability and a new method of drill bit selection

Considering the stratum anti-drilling ability,drill bit working conditions,drill bit application effect and drill bit economic benefits,the similarity of stratum anti-drilling ability was evaluated by grey relational analysis theory to screen out candidate drill bits with reference values.A new comprehensive performance evaluation model of drill bit was established by constructing the absolute ideal solution,changing the relative distance measurement method,and introducing entropy weight to work out the closeness between the candidate drill bits and ideal drill bits and select the reasonable drill bit.Through the construction of absolute ideal solution,improvement of relative distance measurement method and introduction of entropy weight,the inherent defects of TOPSIS decision analysis method,such as non-absolute order,reverse order and unreasonable weight setting,can be overcome.Simple in calculation and easy to understand,the new bit selection method has good adaptability to drill bit selection using dynamic change drill bit database.Field application has proved that the drill bits selected by the new drill bit selection method had significant increase in average rate of penetration,low wear rate,and good compatibility with the drilled formations in actual drilling.This new method of drill bit selection can be used as a technical means to select drill bits with high efficiency,long life and good economics in oilfields.