A new link function in GLM-based control charts to improve monitoring of two-stage processes with Poisson response

In this paper, a new procedure is developed to monitor a two-stage process with a second stage Poisson quality characteristic. In the proposed method, log and square root link functions are first combined to introduce a new link function that establishes a relationship between the Poisson variable of the second stage and the quality characteristic of the first stage. Then, the standardized residual statistic, which is independent of the quality characteristic in the previous stage and follows approximately standardized normal distribution, is computed based on the proposed link function. Then, Shewhart and exponentially weighted moving average (EWMA) cause-selecting charts are utilized to monitor standardized residuals. Finally, two examples and a case study with a Poisson response variable are investigated, and the performance of the charts is evaluated by using average run length (ARL) criterion in comparison with the best literature method.     

Nadir compromise programming: A model for optimization of multi-objective portfolio problem

In problem of portfolio selection, financial Decision Makers (DMs) explain objectives and investment purposes in the frame of multi-objective mathematic problems which are more consistent with decision making realities. At present, various methods have introduced to optimize such problems. One of the optimization methods is the Compromise Programming (CP) method. Considering increasing importance of investment in financial portfolios, we propose a new method, called Nadir Compromising Programming (NCP) by expanding a CP-based method for optimization of multi-objective problems. In order to illustrate NCP performance and operational capability, we implement a case study by selecting a portfolio with 35 stock indices of Iran stock market. Results of comparing the CP method and proposed method under the same conditions indicate that NCP method results are more consistent with DM purposes.     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.     

Green synthesis and characterization of RGO/Cu nanocomposites as photocatalytic degradation of organic pollutants in waste-water

Recently, nanocomposite photocatalysts based on semiconductors have attracted much attention due to their suitable bandgap. Combination of tow of several semiconductors can slow down the electron-hole recombination. In this regard, we have depicted an eco-friendly and green fabrication technique to synthesize RGO/Cu nanocomposite by the reduction of graphene oxide and Cu²⁺ ion utilizing spearmint extract as a reductant and capping agent. The sample was identified by FTIR, XRD, FESEM, EDS, HRTEM, and CV. The results of photocatalytic performance revealed that RGO/Cu is an efficient catalyst for degrading organic pollutants. This compound can eliminate Rhodamine B (RhB) and Methylene blue (MB) 91.0% and 72.0%, respectively.     

Barium cobaltite nanoparticles: Sol-gel synthesis and characterization and their electrochemical hydrogen storage properties

The study of the effect of different chelating agents in the Pechini method on the morphology has been a promising strategy that can be used for practical tuning of the nanoparticle's morphology and hence the electrochemical hydrogen storage capacity. In the current study, the conventional Pechini sol-gel approach was used to prepare the Ba₂Co₉O₁₄ nanoparticles as a novel hydrogen storage material. The X-ray diffraction investigation approved the formation of Ba₂Co₉O₁₄ with a Hexagonal crystal structure for all of the synthesized samples. The scanning electron microscopy (SEM) revealed when citric acid was used as a chelating agent, nanoparticles with finer and more uniform morphology were obtained rather than other chelating sources. The transmission electron microscopy (TEM) showed in the presence of citric acid; the size of the synthesized nanoparticles was between 14 and 24 nm. According to the Diffuse Reflectance Spectroscopy (DRS) analysis, the calculated bandgap of synthesized nanoparticles was approximately 3.2 eV, which indicates that synthesized nanoparticles were semiconductors in essence. The electrochemical hydrogen adsorption/desorption results showed that the sample synthesized by the citric acid has an enhancement in electrochemical hydrogen storage of approximately 800 mAh/g after 15 cycles.     

Hydrogen production by silica membrane reactor during dehydrogenation of methylcyclohexane: CFD analysis

A comprehensive computational fluid dynamic model has been developed using COMSOL Multiphysics 5.4 software to predict the behavior of a membrane reactor in dehydrogenation of methylcyclohexane for hydrogen production. A reliable reaction kinetic of dehydrogenation reaction and a permeation mechanism of hydrogen through silica membrane have been used in computational fluid dynamic modeling. For performance comparison, an equivalent traditional fixed bed reactor without hydrogen removal has been also modeled. After model validation, it has been used to evaluate the operating parameters effect on the performance of both the silica membrane reactor and the equivalent traditional reactor as well. The operating temperature ranged between 473 and 553 K, pressure between 1 and 2.5 bar, sweep factor from ?6.22 to 25 and feed flow rate from 1 to 5 × 10^?6 mol/s. The membrane reactor performed better than the equivalent traditional reactor, achieving as best result complete methylcyclohexane conversion and 96% hydrogen recovery.     

A straightforward one-pot synthesis of Pd–Ag supported on activated carbon as a robust catalyst toward ethanol electrooxidation

Direct Ethanol Fuel Cells (DEFCs) have fascinated remarkable attention on account of their high current density and being environmentally friendly. Developing efficient and durable catalysts with a simple and fast method is a great challenge in the practical applications of DEFCs. To this end, the bimetallic Pd–Ag with adjustable Pd:Ag ratios were synthesized via a simple and one-pot strategy on activated carbon as a support in this study. The Pd–Ag/C catalysts with different molar ratios were synthesized by simultaneous reduction of Pd and Ag ions in the presence of the ethanolic sodium hydroxide as a green reducing agent for the first time. Several different methods, including FE-SEM, HR-TEM, XRD, XPS EDX, ICP-OES, and BET were used to confirm the structure and morphology of the catalysts. The performance of catalysts was also examined in ethanol oxidation. Obtained results of electrochemical experiments revealed that the Pd₃–Ag₁/C catalyst had superior catalytic activity (2911.98 mAmg^?1_Pd), durability, and long-stability compared to the other catalysts. The excellent catalytic characteristic can be attributed to the synergistic effect between Pd and Ag. We presume that our simple method have the chance to be utilized as a proper method for the synthesis of fuel cell catalysts.     

Thermal Response of Steel–Steel Composite Metal Foams under Small-Scale Torch-Fire Conditions

Steel–steel composite metal foam (S–S CMF) is a novel metal matrix composite material characterized by its high strength-to-weight ratio and unique mechanical and thermal properties. It is made up of hollow stainless-steel spheres, embedded in stainless steel matrix, with 65–70% air in the structure making it effective as an insulating material. S–S CMF is being explored for use in tank cars carrying hazardous materials (HAZMATs) as a potential partial replacement for conventional carbon steel and thermal insulating material currently being used. In this study, S–S CMF material is numerically and experimentally evaluated for its thermal protection performance. Experimental studies are conducted in scaled-down jet fire condition while numerical studies are conducted using fire dynamics simulator (FDS). Based on experimental and modeling results, as well as uncertainty studies, 13–15 mm thick S–S CMF ranging in density of about 2.5 g cc⁻¹ tested as novel structural/insulating material meets the acceptance criterion for small-scale simulated torch-fire testing. Further success is anticipated in future full-scale evaluation of 122 × 122 cm samples. The outstanding fire resistance and thermal protection of S–S CMF is attributed to the substantial volume of air trapped within the material, which correlates to its total density.     

Identifying the time of step change in binary profiles

Control charts are intended to aid quality practitioners in monitoring whether a change has occurred in a process. When a control chart indicates an out-of-control signal, it means that the process has changed. However, control chart signals do not indicate the real time of process changes; so estimators are applied to indicate the time when a change in the process takes place, which is referred to as the change point. This paper provides a maximum likelihood estimator to identify the real time of a step change in phase II monitoring of binary profiles, in which the quality of a process is characterized by a logistic regression between the response and predictor variables. Simulation studies are provided to evaluate the effectiveness of the change point estimator.