Multi-objective optimization of rolling schedule based on cost function for tandem cold mill

In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and rolling speeds for a specified product. The proposed schedule optimization model consists of several single cost fi.mctions, which take rolling force, motor power, inter-stand tension and stand reduction into consideration. The cost function, which can evaluate how far the rolling parameters are from the ideal values, was minimized using the Nelder-Mead simplex method. The proposed rolling schedule optimization method has been applied successfully to the 5-stand tandem cold mill in Tangsteel, and the results from a case study show that the proposed method is superior to those based on empirical formulae.

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.

Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.

Fitness of Four-Parameter Beta Distribution Function for Forecasting Gold Reserve and Its Production Lifespan in Ghana

Ghana, renowned for its abundant gold reserves, plays a significant role in the global mining industry. Effective management and accurate forecasting of these reserves are vital for sustainable resource utilization and economic planning. Forecasting gold reserves and estimating their production lifespan are complex tasks that require robust statistical models capable of capturing the underlying dynamics of gold deposit accumulation and extraction. To this end, the four-parameter Beta distribution function emerges as a promising candidate due to its flexibility and ability to handle non-negative data. This research aims to investigate the fitness and applicability of the four-parameter Beta distribution function for forecasting Ghana’s gold reserves and estimating the production lifespan of this precious resource. The empirical paper relied mainly on quarterly secondary datasets on gold reserve between the years 2009 and 2022 secured from the Minerals Commission of Ghana, Accra. Several known statistical distributions including Beta, Weibull, Normal, Logistic and Gamma were explored with Maximum Likelihood Estimation (MLE) and evaluated using model selection criteria as AIC and BIC. Goodness of Fits were evaluated using Kolmogorov-Smirnov Test (K-S), Cramer-Von Mises Statistic and Anderson-Darling Statistic. Based on the analysis conducted, the four-parameter Beta distribution provided the best fit for gold reserve in Ghana. At a 99.9% confidence level and considering the current annual average gold production estimate of 3,700,031.248 to 4,302,647.888 ounces, the projected lifespan of gold production in Ghana extends to the year 1,953,765. This astounding estimate suggests that the country’s gold reserves are expected to sustain production for an extended period, providing a critical resource for economic development and supporting the mining industry well into the distant future.

Fitness of Four-Parameter Beta Distribution Function for Forecasting Gold Reserve and Its Production Lifespan in Ghana

Ghana, renowned for its abundant gold reserves, plays a significant role in the global mining industry. Effective management and accurate forecasting of these reserves are vital for sustainable resource utilization and economic planning. Forecasting gold reserves and estimating their production lifespan are complex tasks that require robust statistical models capable of capturing the underlying dynamics of gold deposit accumulation and extraction. To this end, the four-parameter Beta distribution function emerges as a promising candidate due to its flexibility and ability to handle non-negative data. This research aims to investigate the fitness and applicability of the four-parameter Beta distribution function for forecasting Ghana’s gold reserves and estimating the production lifespan of this precious resource. The empirical paper relied mainly on quarterly secondary datasets on gold reserve between the years 2009 and 2022 secured from the Minerals Commission of Ghana, Accra. Several known statistical distributions including Beta, Weibull, Normal, Logistic and Gamma were explored with Maximum Likelihood Estimation (MLE) and evaluated using model selection criteria as AIC and BIC. Goodness of Fits were evaluated using Kolmogorov-Smirnov Test (K-S), Cramer-Von Mises Statistic and Anderson-Darling Statistic. Based on the analysis conducted, the four-parameter Beta distribution provided the best fit for gold reserve in Ghana. At a 99.9% confidence level and considering the current annual average gold production estimate of 3,700,031.248 to 4,302,647.888 ounces, the projected lifespan of gold production in Ghana extends to the year 1,953,765. This astounding estimate suggests that the country’s gold reserves are expected to sustain production for an extended period, providing a critical resource for economic development and supporting the mining industry well into the distant future.

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.

Optimality for Multi-Objective Programming Involving Arcwise Connected d-Type-I Functions

This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected functions, and examples are given to show the existence of these functions. By utilizing the new concepts, several sufficient optimality conditions and Mond-Weir type duality results are proposed for non-differentiable multi-objective programming problem.

A vague-set-based fuzzy multi-objective decision making model for bidding purchase

A vague-set-based fuzzy multi-objective decision making model is developed for evaluating bidding plans in a bid- ding purchase process. A group of decision-makers (DMs) first independently assess bidding plans according to their experience and preferences, and these assessments may be expressed as linguistic terms, which are then converted to fuzzy numbers. The resulting decision matrices are then transformed to objective membership grade matrices. The lower bound of satisfaction and upper bound of dissatisfaction are used to determine each bidding plan’s supporting, opposing, and neutral objective sets, which together determine the vague value of a bidding plan. Finally, a score function is employed to rank all bidding plans. A new score function based on vague sets is introduced in the model and a novel method is presented for calculating the lower bound of sat- isfaction and upper bound of dissatisfaction. In a vague-set-based fuzzy multi-objective decision making model, different valua- tions for upper and lower bounds of satisfaction usually lead to distinct ranking results. Therefore, it is crucial to effectively contain DMs’ arbitrariness and subjectivity when these values are determined.

Function fitting for modeling seasonal normalized difference vegetation index time series and early forecasting of soybean yield

Forecasting crop yields based on remote sensing data is one of the most important tasks in agriculture.Soybean is the main crop in the Russian Far East.It is desirable to forecast soybean yield as early as possible while maintaining high accuracy.This study aimed to investigate seasonal time series of the normalized difference vegetation index(NDVI) to achieve early forecasting of soybean yield.This research used data from the Moderate Resolution Image Spectroradiometer(MODIS),an arable-land mask obtained from the VEGA-Science web service,and soybean yield data for 2008-2017 for the Jewish Autonomous Region(JAR) districts.Four approximating functions were fitted to model the NDVI time series:Gaussian,double logistic(DL),and quadratic and cubic polynomials.In the period from calendar weeks 22-42(end of May to mid-October),averaged over two districts,the model using the DL function showed the highest accuracy(mean absolute percentage error-4.0%,root mean square error(RMSE)-0.029,P <0.01).The yield forecast accuracy of prediction in the period of weeks 25-30 in JAR municipalities using the parameters of the Gaussian function was higher(P <0.05) than that using the other functions.The mean forecast error for the Gaussian function was 14.9% in week 25(RMSE was0.21 t ha) and 5.1%-12.9% in weeks 26-30(RMSE varied from 0.06 to 0.15 t ha) according to the2013-2017 data.In weeks 31-32,the error was 5.0%-5.4%(RMSE was 0.07 t ha) using the Gaussian parameters and 7.4%-7.7%(RMSE was 0.09-0.11 t ha) for the DL function.When the method was applied to municipal districts of other soy-producing regions of the Russian Far East.RMSE was0.14-0.32 t hain weeks 25-26 and did not exceed 0.20 t hain subsequent weeks.

Overview of multi-objective optimization methods

To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.

Multi-Objective Redundancy Optimization of Continuous-Point Robot Milling Path in Shipbuilding

The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space,low power consumption,and excellent flexibility.However,the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining.In the process of ship construction,the performance of the parts’protective coating needs to bemachined tomeet the Performance Standard of Protective Coatings(PSPC).The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle,greatly reducing machining quality and efficiency.There have been some studies on singleobjective optimization of redundant variables,However,the quality and efficiency of milling are not affected by a single factor,it is usually influenced by several factors,such as the manipulator stiffness,the joint motion smoothness,and the energy consumption.To solve this problem,this paper proposed a new path optimization method for the industrial robot when it is used for five-axis machining.The path smoothness performance index and the energy consumption index are established based on the joint acceleration and the joint velocity,respectively.The path planning issue is formulated as a constrained multi-objective optimization problem by taking into account the constraints of joint limits and singularity avoidance.Then,the path is split into multiple segments for optimization to avoid the slow convergence rate caused by the high dimension.An algorithm combining the non-dominated sorting genetic algorithm(NSGA-II)and the differential evolution(DE)algorithm is employed to solve the above optimization problem.The simulations validate the effectiveness of the algorithm,showing the improvement of smoothness and the reduction of energy consumption.

Multi-objective forest harvesting under sustainable and economic principles

Selective logging is well-recognized as an effective practice in sustainable forest management.However,the ecological efficiency or resilience of the residual stand is often in doubt.Recovery time depends on operational variables,diversity,and forest structure.Selective logging is excellent but is open to changes.This may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary algorithms.The function maximizes remaining stand diversity,merchantable logs,and the inverse of distance between trees for harvesting and log landings points.The Brazilian rainforest database(566 trees)was used to simulate our 216-ha model.The log landing design has a maximum volume limit of 500 m3.The nondominated sorting genetic algorithm was applied to solve the main optimization problem.In parallel,a sub-problem(p-facility allocation)was solved for landing allocation by a genetic algorithm.Pareto frontier analysis was applied to distinguish the gradientsα-economic,β-ecological,andγ-equilibrium.As expected,the solutions have high diameter changes in the residual stand(average removal of approximately 16 m^(3) ha^(-1)).All solutions showed a grouping of trees selected for harvesting,although there was no formation of large clearings(percentage of canopy removal<7%,with an average of 2.5 ind ha^(-1)).There were no differences in floristic composition by preferentially selecting species with greater frequency in the initial stand for harvesting.This implies a lower impact on the demographic rates of the remaining stand.The methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.

Thermodynamic Fit Functions of the Two-Phase Fluid and Critical Exponents

Two-phase fluid properties such as entropy, internal energy, and heat capacity are given by thermodynamically defined fit functions. Each fit function is expressed as a temperature function in terms of a power series expansion about the critical point. The leading term with the critical exponent dominates the temperature variation between the critical and triple points. With β being introduced as the critical exponent for the difference between liquid and vapor densities, it is shown that the critical exponent of each fit function depends (if at all) on β. In particular, the critical exponent of the reciprocal heat capacity c﹣1 is α=1-2β and those of the entropy s and internal energy u are?2β, while that of the reciprocal isothermal compressibility?κ﹣1T is γ=1. It is thus found that in the case of the two-phase fluid the Rushbrooke equation conjectured α +?2β + γ=2 combines the scaling laws resulting from the two relations c=du/dT and?κT=dlnρ/dp. In the context with c, the second temperature derivatives of the chemical potential μ and vapor pressure p are investigated. As the critical point is approached, ﹣d2μ/dT2 diverges as c, while?d2p/dT2 converges to a finite limit. This is explicitly pointed out for the two-phase fluid, water (with β=0.3155). The positive and almost vanishing internal energy of the one-phase fluid at temperatures above and close to the critical point causes conditions for large long-wavelength density fluctuations, which are observed as critical opalescence. For negative values of the internal energy, i.e. the two-phase fluid below the critical point, there are only microscopic density fluctuations. Similar critical phenomena occur when cooling a dilute gas to its Bose-Einstein condensate.

Large Scattered Data Fitting Based on Radial Basis Functions

Solving large radial basis function （RBF） interpolation problem with non-customized methods is computationally expensive and the matrices that occur are typically badly conditioned. In order to avoid these difficulties, we present a fitting based on radial basis functions satisfying side conditions by least squares, although compared with interpolation the method loses some accuracy, it reduces the computational cost largely. Since the fitting accuracy and the non-singularity of coefficient matrix in normal equation are relevant to the uniformity of chosen centers of the fitted RBE we present a choice method of uniform centers. Numerical results confirm the fitting efficiency.

The Application of Multiquadric Function Fitting to Borehole Strain Time Series Data Processing

Based on the existing continuous borehole strain observation,the multiquadric function fitting method was used to deal with time series data. The impact of difference kernel function parameters was discussed to obtain a valuable fitting result,from which the physical connotation of the original data and its possible applications were analyzed.Meanwhile,a brief comparison was made between the results of multiquadric function fitting and polynomial fitting.

GENETIC ALGORITHM WITH FUZZY FITNESS EVALUATION

Using a fuzzy estimator to evaluate the fitness of chromosomes in a genetic algorithm and adaptively training it in the evolutionary process, the genetic algorithm with fuzzy fitness evaluation is proposed to reduce the computation time of the algorithm. An analysis on the optimization performance of the proposed algorithm shows that it maintains good performance with its computation time saved. Finally, simulation results on design of a fuzzy controller are presented.

A Study on the Multi-Objective Optimization Method of Brackets in Ship Structures

The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.

Physical activity,fitness and all-cause mortality:An 18-year follow-up among old people

Background：Little is known about change in physical activity（PA） and its relationship to all-cause mortality among old people.There is even less information about the association between PA,fitness and all-cause mortality among people aged 80 years and above.The objective is to investigate persistence and change in PA over 5 years as a predictor of all-cause mortality,and fitnes as a mediator of this association,among people aged 80 and 85 years at the beginning of an 18-year mortality follow-up period.Methods：Using Evergreen Project data（started in 1989）,4 study groups were formed according to self-reported changes in PA level,over a 5-year period（starting in 1989–1990 and ending in 1994–1995）：remained active（RA,control group）,changed to inactive（CI）,remained inactive（RI）,and changed to active（CA）.Mortality was followed up over the 18-year period（1994–2012）.Cox models with different covariates such as age,sex,use of alcohol,smoking,chronic diseases,and a 10 m walking test were used to analyze the association between change in PA level and mortality.Results：Compared to RA,those who decreased their PA level（CI） between baseline and follow-up had higher all-cause mortality（hazard ratio（HR=2.09;95%CI：1.63–2.69） when adjusted for age,gender,and chronic diseases.RI showed the highest all-cause mortality（HR = 2.16;95%CI：1.59–2.93）.In CA,when compared against RA,the risk of all-cause mortality was not statistically significan（HR=1.51;95%CI：0.95–2.38）.In comparison with RA,when walking speed over 10 m was added as a covariate,all-cause mortality risk was almost statistically significan only in CI（HR=1.37;95%CI：1.00–1.87）.Conclusion：Persistence and change in PA level was associated with mortality.This association was largely explained by fitnes status.Randomized controlled studies are needed to test whether maintaining or increasing PA level could lengthen the life of old people.

Fitness Age Score and the Risk of Long-Term Care Insurance Certification—The Kyoto-Kameoka Longitudinal Study

Long-Term Care Insurance System has been introduced to provide appropriate care for the elderly who needs nursing care in Japan. Physical function is one of most important function for the elderly to live independently. A previous study had established a Fitness Age Score (FAS) based on a theory for biomarker of aging. This study clarified whether the FAS could be used to predict the need for certification of long-term care (CLTC) in independent elderly people. We included 939 independent, community-dwelling-elderly (average age, 74.4 years) whose physical function was measured and for whom the CLTC was tracked for 24 months. The FAS comprised five physical fitness items: Walking time, grip strength, one-leg standing, vertical jump, and functional reach test. Based on the FAS, we calculated the odds ratio (OR) for the CLTC. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) was used for further analysis. We also used the Youden Index (YI), minimum distance method (MD), and two-graph ROC method (TG) to calculate cutoff values (CVs) for screening. We divided subjects into the low and high physical fitness group with CVs. We developed cumulative CLTC curves using the Kaplan-Meier method. Sixteen people could not be tracked for the full 24 months. The measurement of FAS was completed for 798 people, of whom 28 became CLTC. The measurement of FAS was not completed for 125 people, of whom 21 became CLTC. The CLTC OR for people with incomplete FAS was 5.553. The AUC was 0.72 (p < 0.001). The CVs for the FAS were -0.29 for YI (sensitivity, 92.9% and specificity, 40.4%), -1.23 for MD (57.1% and 73.4%), and -0.81 for TG (60.7% and 60.9%). The cumulative CLTC curves according to CVs all showed significant differences (p = 0.000 - 0.020). In conclusion, the FAS can be used to predict CLTC.

Multi-objective robust controller synthesis for discrete-time systems with convex polytopic uncertain domain

Multi-objective robust state-feedback controller synthesis problems for linear discrete-time uncertain systems are addressed. Based on parameter-dependent Lyapunov functions, the Gl2 and GH2 norm expressed in terms of LMI (Linear Matrix Inequality) characterizations are further generalized to cope with the robust analysis for convex polytopic uncertain system. Robust state-feedback controller synthesis conditions are also derived for this class of uncertain systems. Using the above results, multi-objective state-feedback controller synthesis procedures which involve the LMI optimization technique are developed and less conservative than the existing one. An illustrative example verified the validity of the approach.