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.     

Numerical solution of fuzzy relational equations based on smooth fuzzy norms

In this paper, we study and formulate a BP learning algorithm for fuzzy relational neural networks based on smooth fuzzy norms for functions approximation. To elaborate the model behavior more, we have used different fuzzy norms led to a new pair of fuzzy norms. An important practical case in fuzzy relational equations (FREs) is the identification problem which is studied in this work. In this work we employ a neuro-based approach to numerically solve the set of FREs and focus on generalized neurons that use smooth s-norms and t-norms as fuzzy compositional operators.     

A simple model for predicting solid concentration distribution in binary‐solid liquid fluidized beds

A simple mathematical model for predicting the solid concentration profile in binary‐solid liquid fluidized beds is presented. The main assumption is that the solid concentration distribution follows the logistic function, which is supported by the literature. Various equations have been derived to solve key system quantities (e.g., bed expansion height, length and position of the transition zone). In contrast to previous models that often involve adjustable parameters and strongly rely on the availability of experimental data, the present model only requires inputs of fluid and particle properties, operating conditions, and correlations for dispersion and slip velocity. The results showed that the model is applicable to different binary‐solid systems that have size and/or density differences. The model's capability of predicting the layer inversion phenomenon has also been demonstrated. The model is simple but proves capable of accurately predicting key information for the design, operation and scale up of liquid fluidized beds. © 2016 American Institute of Chemical Engineers AIChE J, 63: 469–484, 2017     

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.     

A Frequency Selective Surface With Miniaturized Elements

We demonstrate a new class of bandpass frequency selective surface (FSS), the building block of which, unlike the traditional FSSs, makes use of resonant dipole and slot structures that have dimensions much smaller than the operating wavelength. This design allows localization of bandpass characteristics to within a small area on the surface which in turn facilitates flexible spatial filtering for an arbitrary wave phasefront. The proposed FSS is made up of periodic array of metallic patches separated by thin air-gaps backed by a wire mesh having the same periodicity (Ltlambda). The array of metallic patches constitute a capacitive surface and the wire mesh a coupled inductive surface, which together act as a resonant structure in the path of an incident plane wave. Like traditional FSSs, the capacitive and inductive surfaces of the proposed FSS can easily be fabricated using printed circuit technology on both sides of microwave substrates. It is shown that by cascading such bandpass surfaces in a proper fashion, any arbitrary multipole filter or non-commensurate multiband response can be obtained. The frequency response of the proposed miniaturized-element frequency selective surface (MEFSS) is demonstrated for various incident angles and it is shown that one-pole designs are less sensitive than two-pole designs to the angle of incidence. Dual band designs are also possible based on two-pole designs, but are more sensitive to incident angle than single band designs because of their larger (in terms of wavelengths) spacing. Prototypes of single-pole and dual-pole MEFSSs are fabricated and tested in a waveguide environment at X-band frequencies and excellent agreements between the measured and simulated results are demonstrated     

New competitive results for the stochastic resource-constrained project scheduling problem: exploring the benefits of pre-processing

We study the resource-constrained project scheduling problem with stochastic activity durations. We introduce a new class of scheduling policies for solving this problem, which make a number of a-priori sequencing decisions in a pre-processing phase while the remaining decisions are made dynamically during project execution. The pre-processing decisions entail the addition of extra precedence constraints to the scheduling instance, hereby resolving some potential resource conflicts. We obtain new competitive results for expected-makespan minimization on representative datasets, which are significantly better than those obtained by the existing algorithms when the variability in the activity durations is medium to high.