Optimized interval 2-tuple linguistic aggregation operator based on PGSA and its application in MAGDM

This study proposes a multiple attribute group decisionmaking(MAGDM)approach on the basis of the plant growth simulation algorithm(PGSA)and interval 2-tuple weighted average operators for uncertain linguistic weighted aggregation(ULWA).We provide an example for illustration and verification and compare several aggregation operators to indicate the optimality of the assembly method.In addition,we present two comparisons to demonstrate the practicality and effectiveness of the proposed method.The method can be used not only to aggregate MAGDM problems but also to solve multi-granularity uncertain linguistic information.Its high reliability,easy programming,and high-speed calculation can improve the efficiency of ULWA characteristics.Finally,the proposed method has the exact characteristics for linguistic information processing and can effectively avoid information distortion and loss.

Effectiveness of high intensity interval training on cardiorespiratory fitness and endothelial function in type 2 diabetes:A systematic review

BACKGROUND Type 2 diabetes mellitus(T2DM)is a chronic metabolic syndrome characterized by insulin resistance and hyperglycemia that may lead to endothelial dysfunction,reduced functional capacity and exercise intolerance.Regular aerobic exercise has been promoted as the most beneficial non-pharmacological treatment of cardiovascular diseases.High intensity interval training(HIIT)seems to be superior than moderate-intensity continuous training(MICT)in cardiovascular diseases by improving brachial artery flow-mediated dilation(FMD)and cardiorespiratory fitness to a greater extent.However,the beneficial effects of HIIT in patients with T2DM still remain under investigation and number of studies is limited.AIM To evaluate the effectiveness of high intensity interval training on cardiorespiratory fitness and endothelial function in patients with T2DM.METHODS We performed a search on PubMed,PEDro and CINAHL databases,selecting papers published between December 2012 and December 2022 and identified published randomized controlled trials(RCTs)in the English language that included community or outpatient exercise training programs in patients with T2DM.RCTs were assessed for methodological rigor and risk of bias via the Physiotherapy Evidence Database(PEDro).The primary outcome was peak VO_(2 ) and the secondary outcome was endothelial function assessed either by FMD or other indices of microcirculation.RESULTS Twelve studies were included in our systematic review.The 12 RCTs resulted in 661 participants in total.HIIT was performed in 310 patients(46.8%),MICT to 271 and the rest 80 belonged to the control group.Peak VO_(2 ) increased in 10 out of 12 studies after HIIT.Ten studies compared HIIT with other exercise regimens(MICT or strength endurance)and 4 of them demonstrated additional beneficial effects of HIIT over MICT or other exercise regimens.Moreover,4 studies explored the effects of HIIT on endothelial function and FMD in T2DM patients.In 2 of them,HIIT further improved endothelial function compared to MICT and/or the control group while in the rest 2 studies no differences between HIIT and MICT were observed.CONCLUSION Regular aerobic exercise training has beneficial effects on cardiorespiratory fitness and endothelial function in T2DM patients.HIIT may be superior by improving these parameters to a greater extent than MICT.

Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these fires.Previous methods,i.e.,satellites,are expensive to maintain and cause unnecessary delays.Also,unit-smoke detectors are highly prone to false alerts.In this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model.Two crisp input parameters,namely:FIT and FIG��are used.Results show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression analysis.The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger.As a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts.Through informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.

基于AHP和2-tuple理论的高校教师课堂教学质量评价

课堂教学是整个教学工作的中心环节,要提高高校的教学质量,必须先提高课堂教学质量。开展课堂教学质量评价是提高课堂教学质量的重要手段。因此,本文建立了高校教师课堂教学质量评价模型。首先,建立了高校教师课堂教学质量评价指标体系,在此基础上,利用层次分析法(AHP)确定了评价指标的权重,然后利用二元语义(2-tuple)理论建立了课堂教学质量的总体评价模型,为提高课堂教学质量起到了导向的作用。

Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets

This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function.Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.

Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory

This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range(also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application.

Interval Training and Compensation of Type 2 Diabetes

Introduction: The constant aerobic training is traditionally considered as the best physical activity for diabetic patients. But there is existing problem with adherence (complience) of this type of exercise and toleration of the specific training intensity of exercise for such training time. The advantage of interval training is usage of higher intensity of exercise for very short time alternating with low intensity of exercise. The complex effect of this type of exercise is not mentioned in literature of type 2 diabetes too much. The aim of the study was to find the effect of interval training compound to long term participation of specific exercise program. Methods: 43 obese type 2 diabetes patients treated by diet, oral antidiabetics or insulin were randomized to 2 groups. The control group consisted of 22 patients (12 women, 10 men) with average age 67.4 ± 8.4. 21 patients in main group with average age 65.29 ± 10.67 participated in a controlled exercise program. Before and after the study, both of 2 groups had complex internal investigation including spiroergometry. Results: Fitness parameters improved in this group of diabetics, maximal achieved power in W&middot;kg-1 increased statistically significantly p total cholesterol decreased statistically significantly p < 0.05;average values of LDL-cholesterol decreased about 4.9% and triglycerids about 22.4%;average value of HDL-cholesterol increased about 4.6%;fasting plasma glucose levels decreased about 10.5%. Percentage of body fat p < 0.05 and diastolic blood pressure p < 0.05 decreased based on statistics. BMI tended to decrease but WHR did not change at all. Conclusion: The physical intervention influenced statistically significantly some of the observed parameters. The interval training as a part of physical activities of diabetic patients positively intervenes in complicated system of metabolical processes.

Linguistic Dynamic Modeling and Analysis of Psychological Health State Using Interval Type-2 Fuzzy Sets

The study of psychological health state is helpful to build appropriate models and take effective intervention strategies, and the results benefit the intervened released from psychological distress within the shortest possible time. In this paper, interval type-2 fuzzy sets and fuzzy comprehension evaluation are applied in the analysis of mental health status and crisis intervention. A closed-loop linguistic dynamic intervention model for psychological health state is built. Linguistic dynamic systems based on interval type-2 fuzzy sets are used to describe and analyze the evolutionary process of psychological health status.

Nonsingular Terminal Sliding Mode Control With Ultra-Local Model and Single Input Interval Type-2 Fuzzy Logic Control for Pitch Control of Wind Turbines

As wind energy is becoming one of the fastestgrowing renewable energy resources,controlling large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties.The main goal of the current work is to propose an intelligent control of the wind turbine system without the need for model identification.For this purpose,a novel model-independent nonsingular terminal slidingmode control(MINTSMC)using the basic principles of the ultralocal model(ULM)and combined with the single input interval type-2 fuzzy logic control(SIT2-FLC)is developed for non-linear wind turbine pitch angle control.In the suggested control framework,the MINTSMC scheme is designed to regulate the wind turbine speed rotor,and a sliding-mode(SM)observer is adopted to estimate the unknown phenomena of the ULM.The auxiliary SIT2-FLC is added in the model-independent control structure to improve the rotor speed regulation and compensate for the SM observation estimation error.Extensive examinations and comparative analyses were made using a real-time softwarein-the-loop(RT-SiL)based on the dSPACE 1202 board to appraise the efficiency and applicability of the suggested modelindependent scheme in a real-time testbed.

不同强度训练方式对2型糖尿病超重或肥胖者体成分及糖代谢影响的Meta分析

目的:系统评价高强度间歇训练(high-intensity interval training,HIIT)与中等强度持续性训练(moderate-intensity continuous training,MICT)对2型糖尿病超重或肥胖患者体成分、糖代谢相关指标干预效果,对比两种运动方式的改善效果,为2型糖尿病超重或肥胖患者运动处方的制定提供参考依据。方法:检索The Cochrane Library、PubMed、EMbase、Web of Science、中国知网、中国生物医学文献数据库(CBM)、万方及美国临床试验注册中心数据库,搜集HIIT与MICT对2型糖尿病超重或肥胖患者体成分和糖代谢相关指标干预效果比较的随机对照试验,检索时间为各数据库建库至2022年6月。由2名研究者对所纳入的研究进行筛选、质量评价和数据提取,采用RevMan 5.4软件对结局指标进行Meta分析。结果:①共纳入13项随机对照试验,包括371例患者,所纳入研究整体质量较高。②HIIT组与MICT组对体成分改善效果无显著性差异(体质量:WMD=2.44,95%CI:-3.01-7.89,P>0.05;体质量指数:WMD=0.28,95%CI:-1.21-1.77,P>0.05;腰围:WMD=2.16,95%CI:-2.04-6.35,P>0.05;体脂百分比:WMD=0.47,95%CI:-2.11-3.05,P>0.05);③两组体质量与体质量指数指标“训练周期≥12周”亚组“训练频率≤3次/周”亚组差异有显著性意义(训练周期≥12周亚组:WMD=4.25,95%CI:0.90-7.59,P=0.01;WMD=2.71,95%CI:1.92-3.51,P<0.00001;训练频率≤3次/周亚组:WMD=5.14,95%CI:1.7-8.57,P=0.003;WMD=1.67,95%CI:0.66-2.67,P=0.001);④敏感性分析结果显示,HIIT组与MICT组体脂百分比指标差异有显著性意义(WMD=2.17,95%CI:1.20-3.14,P<0.0001),两组对糖代谢改善效果无显著性差异(空腹血糖:WMD=0.31,95%CI:-0.17-0.79,P>0.05;糖化血红蛋白:WMD=0.01,95%CI:-0.19-0.20,P>0.05;胰岛素抵抗指数:WMD=-0.14,95%CI:-0.71-0.42,P>0.05);⑤亚组分析结果显示,空腹血糖指标在“训练频率≤3次/周”亚组分析中差异有显著性意义(WMD=0.92,95%CI:0.25-1.60,P=0.007);糖化血红蛋白在“训练频率>3次/周”亚组分析中差异有显著性意义(WMD=-0.2,95%CI:-0.3至-0.1,P<0.0001)。结论:①整体而言,HIIT与MICT对改善2型糖尿病超重或肥胖患者体质量、体质量指数、腰围及体脂百分比等体成分和空腹血糖、糖化血红蛋白及胰岛素抵抗指数等糖代谢指标无显著差异;②当训练周期≥12周、训练频率≤3次/周时,MICT对体质量以及体质量指数的改善效果具有一定优势。

A Novel Green Supplier Selection Method Based on the Interval Type-2 Fuzzy Prioritized Choquet Bonferroni Means

In view of the environment competencies,selecting the optimal green supplier is one of the crucial issues for enterprises,and multi-criteria decision-making(MCDM)methodologies can more easily solve this green supplier selection(GSS)problem.In addition,prioritized aggregation(PA)operator can focus on the prioritization relationship over the criteria,Choquet integral(CI)operator can fully take account of the importance of criteria and the interactions among them,and Bonferroni mean(BM)operator can capture the interrelationships of criteria.However,most existing researches cannot simultaneously consider the interactions,interrelationships and prioritizations over the criteria,which are involved in the GSS process.Moreover,the interval type-2 fuzzy set(IT2FS)is a more effective tool to represent the fuzziness.Therefore,based on the advantages of PA,CI,BM and IT2FS,in this paper,the interval type-2 fuzzy prioritized Choquet normalized weighted BM operators with fuzzy measure and generalized prioritized measure are proposed,and some properties are discussed.Then,a novel MCDM approach for GSS based upon the presented operators is developed,and detailed decision steps are given.Finally,the applicability and practicability of the proposed methodology are demonstrated by its application in the shared-bike GSS and by comparisons with other methods.The advantages of the proposed method are that it can consider interactions,interrelationships and prioritizations over the criteria simultaneously.

Robust stability test for 2-D continuous-discrete systems with interval parameters

It is revealed that the dynamic stability of 2-D recursive continuous-discrete systems with interval parameters involves the problem of robust Hurwitz-Schur stability of bivariate polynomials family. It is proved that the Hurwitz-Schur stability of the denominator polynomials of the systems is necessary and sufficient for the asymptotic stability of the 2-D hybrid systems. The 2-D hybrid transformation, i. e. 2-D Laplace-Z transformation, has been proposed to solve the stability analysis of the 2-D continuous-discrete systems, to get the 2-D hybrid transfer functions of the systems. The edge test for the Hurwitz-Schur stability of interval bivariate polynomials is introduced. The Hurwitz-Schur stability of the interval family of 2-D polynomials can be guaranteed by the stability of its finite edge polynomials of the family. An algorithm about the stability test of edge polynomials is given.

Crowdsourcing-Based Framework for Teaching Quality Evaluation and Feedback Using Linguistic 2-Tuple

Crowdsourcing is widely used in various fields to collect goods and services from large participants.Evaluating teaching quality by collecting feedback from experts or students after class is not only delayed but also not accurate.In this paper,we present a crowdsourcing-based framework to evaluate teaching quality in the classroom using a weighted average operator to aggregate information from students’questionnaires described by linguistic 2-tuple terms.Then we define crowd grade based on similarity degree to distinguish contribution from different students and minimize the abnormal students’impact on the evaluation.The crowd grade would be updated at the end of each feedback so it can guarantee the evaluation accurately.Moreover,a simulated case is shown to illustrate how to apply this framework to assess teaching quality in the classroom.Finally,we developed a prototype and carried out some experiments on a series of real questionnaires and two sets of modified data.The results show that teachers can locate the weak points of teaching and furthermore to identify the abnormal students to improve the teaching quality.Meanwhile,our approach provides a strong tolerance for the abnormal student to make the evaluation more accurate.

2-Tuple and Rough Set Based Reduction Model for Multi-sensory Evaluation Indicators

In order to lessen adverse influences of excessive evaluative indicators of the initial set in multi-sensory evaluation,a2-tuple and rough set based reduction model is built to simplify the initial set of evaluative indicators. In the model,a great variety of descriptive forms of the multi-sensory evaluation are also taken into consideration. As a result,the method proves effective in reducing redundant indexes and minimizing index overlaps without compromising the integrity of the evaluation system. By applying the model in a multi-sensory evaluation involving community public information service facilities,the research shows that the results are satisfactory when using genetic algorithm optimized BP neural network as a calculation tool. It shows that using the reduced and simplified set of indicators has a better predication performance than the initial set,and 2-tuple and rough set based model offers an efficient way to reduce indicator redundancy and improves prediction capability of the evaluation model.

Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards optima.The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS.The antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm.Tuning of the consequent part parameters are accomplished using extreme learning machine.The optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices.The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm.Analysis of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

Sustainability Evaluation of Modern Photovoltaic Agriculture Based on Interval Type-2 Fuzzy AHP-TOPSIS and Least Squares Support Vector Machine Optimized by Fireworks Algorithm

Photovoltaics(PV)has been combined with many other industries,such as agriculture.But there are many problems for the sustainability of PV agriculture.Timely and accurate sustainability evaluation of modern photovoltaic agriculture is of great significance for accelerating the sustainable development of modern photovoltaic agriculture.In order to improve the timeliness and accuracy of evaluation,this paper proposes an evaluation model based on interval type-2 Fuzzy AHP-TOPSIS and least squares support vector machine optimized by fireworks algorithm.Firstly,the criteria system of modern photovoltaic agriculture sustainability is constructed from three dimensions including technology sustainability,economic sustainability and social sustainability.Then,analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)methods are improved by using interval type-2 fuzzy theory,and the traditional evaluation model based on interval type-2 Fuzzy AHP-TOPSIS is obtained,and the improved model is used for comprehensive evaluation.After that,the optimal parameters of least squares support vector machine(LSSVM)model are obtained by Fireworks algorithm(FWA)training,and the intelligent evaluationmodel for the sustainability of modern photovoltaic agriculture is constructed to realize fast and intelligent calculation.Finally,an empirical analysis is conducted to demonstrate the scientificity and accuracy of the proposed model.This study is conducive to the comprehensive evaluation of the sustainability of modern photovoltaic agriculture,and can provide decision-making support for more reasonable development model in the future of modern photovoltaic agriculture.

A Note on SK, SK₁, SK₂Indices of Interval Weighted Graphs

In this study, the SK, SK1 and SK2 indices are defined on weighted graphs. Then, the SK, SK1 and SK2 indices are defined on interval weighted graphs. Their behaviors are investigated under some graph operations by using these definitions.

Effect of high-intensity interval training on blood glucose control, adipocytokine secretion and oxidative stress response in patients with T2DM

Objective:To investigate the effect of high-intensity interval training on blood glucose control, adipocytokine secretion and oxidative stress response in patients with T2DM.Methods: A total of 92 patients with newly diagnosed T2DM who were treated in this hospital between July 2016 and July 2017 were divided into the control group (n=46) and HIIT group (n=46) by random number table method. Control group received conventional hypoglycemic therapy, HIIT group received hypoglycemic combined with high-intensity interval training therapy, and the intervention lasted for 3 months. The differences in blood glucose control, adipocytokine secretion and oxidative stress response were compared between the two groups before and after intervention.Results: Before intervention, the differences in blood glucose index levels in peripheral blood as well as the contents of adipocytokines and oxidative stress indexes in serum were not statistically significant between the two groups of patients. After 3 months of intervention, blood glucose indexes FPG, FINS and 2hPBG levels in peripheral blood of HIIT group were lower than those of control group;adipocytokine APN content in serum was higher than that of control group whereas LEP, Resistin and CHEM contents were lower than those of control group;oxidative stress indexes MDA and ROS contents in serum were lower than those of control group whereas T-AOC content was higher than that of control group. Conclusion: Routine hypoglycemic therapy combined with high-intensity interval training can further control the blood glucose levels, regulate the secretion of adipocytokines and reduce the systemic oxidative stress response.