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Due to the growth of the elderly population, age-related neurological disorders are an increasing problem. Aging begins very gradually and later leads to several neurological issues such as lower neurotransmitter levels, oxidative stress, neuronal inflammation, and continual neuronal loss. These changes might contribute to brain disorders such as Alzheimer’s disease (AD), dementia or mild cognitive impairment, and epilepsy and glioma, and can also aggravate these disorders if they were previously present. Momordica charantia (bitter gourd), a member of the Cucurbitaceae family, is a good source of carbohydrates, proteins, vitamins, and minerals. It is used for diabetes and known for its hypoglycemic and antioxidant effects. In this review, we discuss the pharmaceutical effects of M. charantia on age-related neurological disorders. We searched several databases, including PubMed and Google Scholar, using MeSH terms. We searched articles published up until 2022 regardless of publication language. M. charantia is rich in luteolin, which increases acetylcholine in neurons by binding to enzymes in acetylcholine metabolism pathways, including butyrylcholinesterase and acetylcholinesterase. This binding inhibits the hyperphosphorylation of tau protein by restraining its kinase enzyme. Furthermore, this substance can lower serum cholesterol and has multi-target activity in AD and memory loss. M. charantia can also improve memory by decreasing tau protein and it also has potent antioxidant activity and anti-inflammatory effects. This review highlights that M. charantia has effects on many age-related neurological disorders, and can be a cost-effective supplement with minimal side effects.

In some statistical processes monitoring (SPM) applications, relationship between two or more ordinal factors is shown by an ordinal contingency table (OCT) and it is described by the ordinal Log-linear model (OLLM). Newton-Raphson algorithm methods have also been used to estimate the parameters of the log-linear model. In this paper, the OLLM based processes is monitored using MR and likelihood ratio test (LRT) approaches in Phase I. Some simulation studies are applied to performance evaluation of the proposed approaches in terms of probability of signal under step shifts, drifts and the presence of outliers. Results show that, by imposing the small and moderate shifts in the ordinal log-linear model parameters, the MR statistic has better performance than LRT. In addition, a real case study in dissolution testing in pharmaceutical industry is employed to show the application of the proposed control charts in Phase I.

Nowadays, the optimal use of energy, speed increase, fuel consumption reduction and environmental pollution prevention are essential in the ship design and manufacturing discussion. So it is critical to be able to estimate ship's response to waves, since the resulting added resistance and increasing fuel consumption also increasing environmental pollution. In this study, the numerical solution of the Iran-Bushehr container ship model was determined to simulate the free surface flow around the hull and estimate the total resistance and investigate ship motions in 2 degree-of-freedom (heave and pitch) in regular head waves. Finally, the results of simulation in different conditions are presented and compared. KCS container ship used for validation. CFD codes in Star-CCM+ used to calculate three dimensional, incompressible, unsteady RANS equations.

Abstract In some applications of statistical process monitoring, a quality characteristic can be characterized by linear regression relationships between several response variables and one explanatory variable, which is referred to as a “multivariate simple linear profile.” It is usually assumed that the process parameters are known in Phase II. However, in most applications, this assumption is violated; the parameters are unknown and should be estimated based on historical data sets in Phase I. This study aims to compare the effect of parameter estimation on the performance of three Phase II approaches for monitoring multivariate simple linear profiles, designated as MEWMA, MEWMA_3 and $${\text{MEWMA}}/\chi^{2}$$ MEWMA / χ 2 . Three metrics are used to accomplish this objective: AARL, SDARL and CVARL. The superior method may be different in terms of the AARL and SDARL metrics. Using the CVARL metric helps practitioners make reliable decisions. The comparisons are carried out under both in-control and out-of-control conditions for all competing approaches. The corrected limits are also obtained by a Monte Carlo simulation in order to decrease the required number of Phase I samples for parameter estimation. The results reveal that parameter estimation strongly affects the in-control and out-of-control performance of monitoring approaches, and a large number of Phase I samples are needed to achieve a parameter estimation that is close to the known parameters. The simulation results show that the MEWMA and $${\text{MEWMA}}/\chi^{2}$$ MEWMA / χ 2 methods perform better than the MEWMA_3 method in terms of the CVARL metric. However, the superior approach is different in terms of AARL and SDARL.

In this paper, a new robust method based on weighted maximum likelihood estimation (WMLE) is proposed to estimate the regression parameters in logistic profiles in Phase I. This approach reduces the outlier’s effects on the statistical performance of T2 control chart in terms of probability of Type I error. A numerical example is used to evaluate the performance of the proposed method. The results show the better performance of the proposed estimator compared to the maximum likelihood estimation method in terms of power in T2 control chart. Introduction: Yeh et al. (2009) proposed five based T2 statistics to monitor the binary logistic regression profile in Phase I. Different approaches are proposed to monitor logistic regression profiles in Phase II. So far, few researches have been done on monitoring the profile with the presence of contaminated data. In this area, Ebadi and Shahriari (2014) proposed robust estimation approach to monitor the simple linear profile based on two classic and robust methods (M-estimator) with two functions including Huber weighted and double square functions. The aim of this paper is to monitor the logistic regression profiles with the presence of outliers in Phase I based on weighted maximum likelihood robust estimator and T2 control chart. The main questions of this papers are as follows: a) Evaluating the effect of outliers on the mean and standard deviation of the proposed and classic estimators of the logistic regression profile parameters and probability of Type I error in common T2 control chart, b) Comparing performance of the proposed and classic estimators on the T2 control chart power for different shifts in logistic regression profile parameters under outliers in Phase I. Materials and Methods: Sometimes, there are outliers in the gathered data which lead to incorrect estimation of the profile parameters. Hence, to decrease or remove the effect of outlier(s), robust estimation methods are applied. In this paper, a robust approach called weighted maximum likelihood estimator (WMLE) is applied to estimate the parameters of the logistic regression profiles as follows (Maronna et al., 2006): (1) where, is the probability of response variable in each level of logistic regression profile using the estimated parameters. A robust estimate for obtaining parameters is achieved by minimizing the above function. However, in order to give less weight to outliers, we can consider the following relationship and minimize it. (2) where is the weight of the ith observation which is calculated as Equation (3) (3) in which W is a non-ascending function and computed based on Carroll and Pederson (1993) as follows: (4) Results and Discussion The Type I error probability of T2 control chart considering the outlier using MLE and WMLE methods is summarized in Table 1. Table 1- Type I error probability of T2 control chart considering MLE and WMLE methods WMLE MLE Estimation Method 0.0718 0.1242 Type I error probability In this section, r percentage of the total data is contaminated with an increase in the variance of the errors. For r equal to 0.07 and 0.15, the variance error is changed from 1 to 4 and Type I error probability of the T2 control chart with both classic and proposed estimators are calculated and reported in Table 2. Table 2- Type I error probability of the T2 control chart with both classic and proposed estimators under different r Type I error probability Estimation methods r=0.07 Type I error probability Estimation methods r=0.15 0.1801 MLE 0.2779 MLE 0.1021 WMLE 0.1541 WMLE Based on Table 2, Type I error probability of the T2 control chart under the classic method is more than the robust one and this result shows the better performance of the proposed method rather than the classic one. Conclusion In this paper, a robust approach was developed to estimate the logistic regression profiles with the presence of outliers in Phase I. The performance of the proposed robust estimator was compared with the classic method (MLE) based on Type I error probability and power of T2 control chart in Phase I. Results showed that the WMLE method outperforms the MLE in estimating the logistic regression profile parameter under outliers. References Ebadi, M., & Shahriari, H., (2014), "Robust Estimation of Parameters in Simple Linear Profiles Using M-Estimators", Communications in Statistics - Theory and Methods, 43(20), 4308-4323. Maronna, AR., Martin, R.D., & Yohai, V.J., (2006), Robust Statistics Theory and Methods, John Wiley, New York. Yeh A.B., Huwang L., & Li Y.M., (2009), "Profile Monitoring for a Binary Response", IIE Transactions, 41(13), 931-941.

In this paper we propose an integrated algorithm based on combination of a discrete- event simulation and genetic algorithm. The simulation model is considered as a constraint-satisfaction procedure and if the streaming operations are initiated, then the meta-heuristic takes predefined steps to improve the solution. The latter is constructed through an interface, namely control matrix, implemented as interaction between the simulation model and refined solution of meta-heuristic. In run-time, the control matrix is accessed via simulation model for further modifications. The proposed method is implemented on classical job-shop problems with objective of makespan and results are compared with mixed integer programming model. Moreover, the appropriate dispatching priorities are achieved for dynamic job-shop problem minimizing a multi-objective criteria. The results show that simulation-based optimization are highly capable to capture the main characteristics of the shop and produce optimal/near-optimal solutions with highly credibility degree.

In most manufacturing processes, each product may contain a variety of quality characteristics which are of the interest to be optimized simultaneously through determination of the optimum setting of controllable factors. Although, classic experimental design presents some solutions for this regard, in a fuzzy environment, and in cases where the response data follow non-normal distributions, the available methods do not apply any more. In this paper, a general framework is introduced in which NORTA inverse transformation technique and fuzzy goal programming are used to deal with non-normality distribution of the response data and the fuzziness of response targets respectively. Moreover, the presented framework uses a simulation approach to investigate the effectiveness of the determined setting of controllable factors obtained from statistical analysis, for optimization of sink mark index, deflection rate and volumetric shrinkage in plastic molding manufacturing processes. The accuracy of the proposed method is verified through a real case study.

This paper addresses a reliable facility location problem with considering facility capacity constraints. In reliable facility location problem some facilities may become unavailable from time to time. If a facility fails, its clients should refer to other facilities by paying the cost of retransfer to these facilities. Hence, the fail of facilities leads to disruptions in facility location decisions and this problem is an attempt to reducing the impact of these disruptions. In order to formulate the problem, a new mixed-integer nonlinear programming (MINLP) model with the objective of minimizing total investment and operational costs is presented. Due to complexity of MINLP model, two different heuristic procedures based on mathematical model are developed. Finally, the performance of the proposed heuristic methods is evaluated through executive numerical example. The numerical results show that the proposed heuristic methods are efficient and provide suitable solutions.

In this paper, a new approach based on game theory has been proposed to multi responses problem optimization. Game theory is a useful tool for decision making in the conflict of interests between intelligent players in order to select the best joint strategy for them through selecting the best joint desirability. Present research uses the game theory approach via definition of each response as each player and factors as strategies of each player. This approach cans determine the best predictor factor sets in order to obtain the best joint desirability of responses. For this aim, the signal to noise ratio(SN) index for each response have been calculated with considering the joint values of strategies then obtained SN ratios for each strategy is modeled in the game theory table. Finally, using Nash Equilibrium method, the best strategy which is the best values of predictor factors is determined. A real case and a numerical example are given to show the efficiency of the proposed method. In addition, the performance of the proposed method is compared with the VIKOR method.

In some statistical process control applications, quality of a process or product can be characterized by a relationship between a response and one or more independent variables, which is typically referred to a profile. In this paper, polynomial profiles are considered to monitor processes in which there is a first order autoregressive relation between the error terms in each profile. A remedial measure is first proposed to eliminate the effect of autocorrelation in phase-ІІ monitoring of autocorrelated profiles. Then, three methods are employed to monitor polynomial profiles where their performances are compared using the average run length criterion.

Identification of a real time of a change in a process, when an out-of-control signal is present is significant. This may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. Another popular topic in the Statistical Process Control (SPC) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropriate for discussing the quality of processes or products. One, rather, uses a relationship between a response variable and one or more explanatory variable for this purpose. In this paper, the concept of Maximum Likelihood Estimator (MLE) applied to estimate of the change point in binary profiles, when the type of change is drift. Simulation studies are provided to evaluate the effectiveness of the change point estimator.

Profile monitoring is a new research subject in Statistical Process Control which has been recently considered by many researchers. Profile describes the relationship between a response variable and one or more independent variables. This relationship is often modeled using regression which can be simple linear, multiple linear, polynomial or sometimes nonlinear. To monitor polynomial profiles, some methods have been developed, the best of which is the orthogonal polynomial approach. One of the disadvantages of this method is the large number of control charts which are simultaneously used. In this paper, a new approach has been proposed based on orthogonal polynomial approach, in which only two control charts are used to monitor a kth-order polynomial profile. Simulation findings and average run length (ARL) curve analysis imply better performance of the proposed approach compared to the existing approach. Also, using the proposed approach is much easier in practice.

Nowadays in some manufacturing processes, the quality of a product or process is well expressed by both correlated attribute and variable quality characteristics. To best of our knowledge, there is no method for monitoring the covariance matrix of multivariate-attribute quality characteristics. In this paper, we propose a multi-layer perception artificial neural network to monitor multivariate-attribute processes as well as to diagnose the quality characteristic(s) responsible for out-of-control signals. The performance of the proposed method is evaluated through a numerical example from both detection and diagnosis perspectives. In addition, the performance of the proposed neural network in detecting shifts in the variance of quality characteristics is compared with two statistical methods first proposed for monitoring the variability of multivariate quality characteristics and developed in this paper for our problem. The results of numerical example show that the proposed artificial neural network outperforms the extended statistical methods in detecting different out-of-control shifts. The results also confirm that the performance of the proposed neural network in identifying the quality characteristic(s) responsible for out-of-control signal is satisfactory