Statistical Optimization for Production of Light Olefins in a Fluidized‐Bed Reactor

The methanol‐to‐olefins reaction (MTO) was studied in a small‐scale fluidized‐bed reactor over synthesized silicoaluminophosphate (SAPO‐34) catalysts. Mesoporous nanocrystalline SAPO‐34 molecular sieves were synthesized hydrothermally by ultrasonic and microwave‐assisted aging processes in the presence of hexadecyltrimethylammonium bromide (CTAB) and tetradecyldimethyl(3‐trimethoxysilylpropyl)ammonium chloride (TPOAC) as surfactant agents. The Box‐Behnken experimental design method was applied to determine the optimum operating parameters of this process conducted in the fluidized‐bed reactor. The optimum conditions in terms of reaction temperature, ratio of inlet gas velocity to minimum fluidizing velocity, and MeOH weight fraction were evaluated.     

Water-soluble Copolymers of N-vinylpyrrolidone and Vinyl Acetate: Synthesis, Characterization, and Monomer Reactivity at High Conversions

Homogeneous copolymers of N-vinylpyrrolidone (VP) and vinyl acetate (VA) which form clear aqueous solutions were prepared by free radical polymerization in a solution of isopropanol alcohol, using 2,2-azobisisobutyronitrile as an initiator. They were characterized by FTIR, ¹H-NMR, and element analysis studies. The reactivity ratios of the monomer were computed by the Extended Kelen–Tüdós method at high conversions, using data from both ¹H-NMR and elemental analysis studies. The reactivity ratios of VP and VA in a homogenous copolymer were observed to be very different from that of a heterogeneous copolymer. Additional information was obtained by finding out the sequence length distribution for copolymers.     

Direct Measurement of Transformation Zone Strains in Toughened Zirconia

Residual strains responsible for crack tip shielding have been measured within transformation zones surrounding cracks in Mg-PSZ. Two techniques were used for strain measurement: moiré interferometry and high-resolution image matching. Both methods provide maps of differential in-plane displacements within the specimen surface intersected by the crack, the latter method with the higher resolution. The results are compared with finite-element analysis to assess surface relaxation effects, and the measured strains are used to evaluate the crack tip shielding stress intensity factor. Calculations based on the assumption that the unconstrained transformation strain is hydrostatic dilatation yielded results that were significantly smaller than the measured toughness increases.     

Implementation of optical perfect-shuffle module

Experimental details are presented of the measured performance of a novel implementation of a cascadable module for the one-dimensional perfect shuffle of a two-dimensional array of optical signals. The design uses an anamorphic telescope to correct the difference in pitch in the two axes resulting from an image split and recombine operation performed with patterned mirrors. The performance of the cylindrical lens limits the scalability of the design.     

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

     

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.     

Components for the implementation of free-space optical crossbars

We describe an optical system developed to form the basis of a 64 × 64 free-space optical matrix-matrix crossbar switch. The design and performance of each of the main optical components is discussed: lenses, diffractive optical elements, and polarizing beamsplitters, together with the optomechanical hardware design. For these components, throughput levels of -6.9 dB have been achieved, which is compatible with full system operation at 10(-12) bit error rates at ≥270 Mbits s(-1).     

Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm

Analysis of customer interactions for electronic customer relationship management (e-CRM) can be performed by way of using data mining (DM), optimization methods, or combined approaches. The microeconomic framework for data mining addresses maximizing the overall utility of an enterprise where transaction of a customer is a function of the data available on that customer. In this paper, we investigate an alternative problem formulation for the catalog segmentation problem. Moreover, a self-adaptive genetic algorithm has been developed to solve the problem. It includes clever features to avoid getting trapped in a local optimum. The results of an extensive computational study using real and synthetic data sets show the performance of the algorithm. In comparison with classical catalog segmentation algorithms, the proposed approach achieves better performance in Fitness and CPU-time.     

Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering

In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.