A strategy for the electro-organic synthesis of new hydrocaffeic acid derivatives

Electrochemical treatment processes can significantly contribute to the protection of the environment through the minimization of waste and toxic materials in effluents. From a pharmaceutical point of view and due to the existing resemblance between the electrochemical and biological reactions, it can be assumed that the oxidation mechanisms on the electrode and in the body share similar principles. In this paper, the application of electrochemical studies in the design of an environmentally friendly method was delineated for the new hydrocaffeic acid (HCA, 3,4-dihydroxy hydrocinnamic acid) derivatives synthesis at carbon electrodes in an undivided cell. In this cell, the EC mechanism reaction was involved, comprising two steps alternatively; (1) electrochemical oxidation and (2) chemical reaction. In particular, the electro-organic reactions of HCA, an important biological molecule, were studied in a water–acetonitrile (90:10 v/v) mixture in the presence of benzenesulfinic acid (3) and p-toluenesulfinic acid (4). The research included the use of a variety of experimental techniques, such as cyclic voltammetry, controlled-potential electrolysis and product spectroscopic identification.     

Preparation,characterization and in vitro drug release properties of polytrimethylene carbonate/polyadipic anhydride blend microspheres

Polyadipic anhydride (PAA), an aliphatic polyanhydride, and polytrimethylene carbonate (PTMC), an aliphatic polycarbonate, were synthesized via ring opening polymerization of oxepan‐2,7‐dione and melt‐condensation of trimethylene carbonate (1,3 dioxan‐2‐one), respectively. PTMC–PAA blend microspheres containing different ratios of buprenorphine HCl (2, 5, and 10%) were prepared by an oil‐in‐oil emulsion solvent removal method. Microspheres with different ratios of PTMC–PAA (85/15, 70/30, and 55/45) containing 5% buprenorphine HCl were prepared. Microspheres were spherical with visible cracks and pores on the surface. The average particle size of microspheres was around 200 μm for all microspheres. Drug loading efficiency of PTMC–PAA microspheres (85/15, 70/30, and 55/45) was 97.2, 95.2, and 70.2%, respectively. With the increase in the PTMC ratio, the melting point and the enthalpy of melting were both decreased. The mechanism for drug release from PTMC–PAA blend microspheres were generally a combination of drug diffusion through polymers and biodegradation of the polymers. In first three days, the release from microspheres followed zero order kinetics and was dependent on the PAA content. After three days the drug release from microspheres followed first order kinetics. In conclusion it was demonstrated that buprenorphine HCl release from microspheres could be successfully controlled by using different ratios of PTMC–PAA blends. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2377–2383, 2006     

Change Point Estimation in the Mean of Multivariate Linear Profiles with No Change Type Assumption via Dynamic Linear Model

Change point estimation is a useful concept that helps quality engineers to effectively search for assignable causes and improve quality of the process or product. In this paper, the maximum likelihood approach is developed to estimate change point in the mean of multivariate linear profiles in Phase II. After the change point, parameters are estimated through filtering and smoothing approaches in dynamic linear model. The proposed change point estimator can be applied without any prior knowledge about the change type against existing estimators which assume change type is known in advance. Besides, sporadic change point can be identified as well. Simulation results show the effectiveness of the proposed estimators to estimate step, drift and monotonic, as well as sporadic changes in small to large shifts. In addition, effect of different values of the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart smoothing coefficient on the performance of the proposed estimator is investigated presenting that the smoothing estimator has more uniform performance. Copyright © 2015 John Wiley & Sons, Ltd.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

An integrated supervised learning solution for monitoring process mean vector

When an out-of-control condition is detected by a control chart, a search begins to identify and eliminate the source(s) of the signal. Identification of the time when a process first changed is an important step in root cause analysis which helps a process engineer to eliminate the source(s) of assignable cause effectively. The time when a change takes place in the process is referred to as the change point. In multivariate environment, since there is more than one variable involved, then root cause analysis is relatively harder compared to the case of univariate because it is not clear exactly which variable has contributed to the out-of-control condition and in what direction its mean has shifted. Hence, a procedure that identifies the change point, performs diagnostic analysis, and specifies the direction of the shift in the mean of the contributing variable(s) all simultaneously could help to conduct root cause analysis effectively. Although different multivariate methods exist in the literature that allow to either estimate change point in the process mean vector or identify the contributing variables leading to the out-of-control condition, but in this research, an integrated supervised learning solution is proposed, which helps to (1) detect of an out-of-control condition, (2) identify the change point leading to shift in the mean vector, (3) specify the variable(s) contributing to the out-of-condition, and (4) identify the direction of the shift in the mean of each contributing variable simultaneously. A real case study is used to evaluate and compare the performance of the proposed integrated approach to existing methods in the literature.     

Identifying the period of a step change in high‐yield processes

Quality control charts have proven to be very effective in detecting out‐of‐control states. When a signal is detected a search begins to identify and eliminate the source(s) of the signal. A critical issue that keeps the mind of the process engineer busy at this point is determining the time when the process first changed. Knowing when the process first changed can assist process engineers to focus efforts effectively on eliminating the source(s) of the signal. The time when a change in the process takes place is referred to as the change point. This paper provides an estimator for a period of time in which a step change in the process non‐conformity proportion in high‐yield processes occurs. In such processes, the number of items until the occurrence of the first non‐conforming item can be modeled by a geometric distribution. The performance of the proposed model is investigated through several numerical examples. The results indicate that the proposed estimator provides a reasonable estimate for the period when the step change occurred at the process non‐conformity level. Copyright © 2009 John Wiley & Sons, Ltd.     

Identifying change point of a non-random pattern on control chart using artificial neural networks

An important step in root cause analysis is the identification of the time when process first changed. The time when a disturbance first manifested itself into the process is referred to as change point. Identification of the change point could help process engineer to perform root cause analysis effectively. In this paper, an estimator for the change point of a normal process mean using artificial neural network (ANN) is proposed. Five patterns of change namely single step, linear trend, systematic, cyclic, and mixture are studied. Whenever possible, results are compared numerically to the results obtained by other methods proposed by different researchers. First the type of change to be recognized by an ANN-based pattern recognizer is identified and then the change point in the process mean is estimated. Results indicate satisfactory performance for the proposed method that could be used as an effective method for root cause analysis by process engineer.