A Regioselective Approach to Synthesize Indolyl Diketone Derivatives via Magnetic Polymeric Copper-Catalyst

Catalysis Letters - In the present paper, an efficient Cu-catalyzed regioselective acylation of indoles with phenylglyoxals was developed which is the first example of indolyl diketones synthesis...     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

Maintenance Scheduling Optimization in a Multiple Production Line Considering Human Error

An analytical multiobjective maintenance planning model that maximizes reliability while minimizing cost and human error is proposed. In order to incorporate human error, the model minimizes the maximum human error over the planning horizon. Human Error Assessment and Reduction Technique (HEART) is used to quantify the human error. Maintenance activities include adjustment and replacement activities, in which each of them consumes a certain amount of human resource, spare parts, and budget and brings about a specified level of reliability and human error. Economic dependence is also considered, in which grouping maintenance activities reduces total cost. However, this may increase human error probability due to operator fatigue or time pressure. The main purpose is to investigate the relationship between human factors and maintenance activities to find the preferred maintenance plan. A multiple production line is considered as a case study. A sensitivity analysis is performed, and the effects of grouping and human factors on the preferred maintenance plan are discussed. It is shown how human proficiency may affect reliability and cost.     

Preparation and Characterization of Magnetite–Chitosan Nanoparticles and Evaluation of Their Cytotoxicity Effects on MCF7 and Fibroblast Cells

Nowadays, magnetic nanoparticles play a key role in drug treatment. However, if they are uncoated, they might aggregate and cause embolism in drug delivery. To avoid this, they are generally coated with polymers. In this investigation, magnetic nanoparticles (MNPs) were coated with chitosan through in situ co-precipitation process and their properties in addition to their cytotoxicity in MCF7 and fibroblast cells were analyzed. The results showed MNPs–chitosan nanoparticles would contain an average mean diameter of 32 nm and a saturation magnetization (M _S) value of 38.87 emu/g. To characterize the synthesized nanoparticles in terms of structural, morphological and magnetic properties, Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), thermal gravimetric analysis (TGA) and vibrating sample magnetometry (VSM) studies were used. Toxicity test was also done to investigate whether their presence would cause any toxicity in the body; in addition, antibacterial assay was done to analyze if nanoparticles could be used as an antimicrobial agent.     

Reducing CO2 emission from exhaust gases using molten carbonate fuel cells: a new approach

The aim of this study is to couple molten carbonate fuel cell (MCFC) stack with integrated gasification combined cycle fed by refinery residues, to remove CO₂ from gas turbine exhaust gases that have CO₂ emission rate of 14,200 ton/year. By applying multi-objective optimisation (MOO) using genetic algorithm, the optimal values of operating load and the corresponding values of objective functions are obtained. The MOO of the MCFC system regarding two scenarios is performed. The first scenario is minimisation of cost of electricity (COE) and CO₂ emission rate. Objective functions of the second scenario are the same as in the first scenario while CO₂ tax is taken into account. Results show that the second scenario has 29.5% lower average optimal COE and 2.5% lower average emission rate in comparison with the first scenario. A sensitivity analysis is also performed to study the effect of fuel price and CO₂ tax variations on optimal solutions.     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.     

Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.     

In situ emulsion polymerization and characterization of PVAc nanocomposites including colloidal silica nanoparticles for wood specimens bonding

Polyvinyl acetate (PVAc) nanocomposites for wood adhesives containing different amounts of colloidal silica nanoparticles (CSNs) were synthesized via in situ one-step emulsion polymerization. The adhesion strength of wood specimens bonded by PVAc nanocomposites was investigated by the tensile test. Thermal properties of PVAc nanocomposites were also characterized by differential scanning calorimetry and thermogravimetric analysis. Rheological and morphological properties of the PVAc nanocomposites were investigated using rheometric mechanical spectrometry and field emission scanning electron microscopy (FESEM), respectively. The obtaining results showed that the shear strength of PVAc nanocomposite including 1 wt. % CSNs has the highest shear and tensile strength about 4.7 and 3.2 MPa, respectively. A small increment of T_g (~3 °C) and considerable increment of the ash content proved the enhancement of PVAc thermal characterization in the presence of CSNs. FESEM results showed uniform dispersion of nanoparticles throughout the PVAc matrix due to using the in situ emulsion polymerization process. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48570.     

Numerical analysis of plastic deformation of a circular sheet metal subjected to transverse impact loading

Based on the concept of Tensor Code a numerical method is presented for analysis of the plastic deformation process of circular sheet metals subjected to transverse impact loading. In the solution process, the equation of motion is solved explicitly with finite difference method in a series of small time steps over a Lagrangian mesh of zones. Using this method, a code has been developed and utilized for investigation of the deformation behavior of an explosively loaded circular sheet metal under various conditions. Deformation characteristics of a sheet under rectangular and triangular pressure distributions are discussed. It is shown that when these simple distributions are combined with each other, their individual effects on the deformation behavior are also combined in the deformation process. Effects of shape and duration of pressure pulse as well as boundary conditions have been explored. Moreover, results of the numerical simulation have been compared with those of theoretical solution and experiments reported by other researchers. Good agreements between them show the validity of the developed code.