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The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score.

A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach.

This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately.

In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data.

The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc.

For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

The purpose of this paper is to investigate the surface properties, particle sizes and corrosion inhibition performance of sodium dodecyl sulfate (SDS) in the presence of imidazolium-based ionic liquid as an additive. Up to now, different properties of alone surfactants and ionic liquids have been studied. However, few studies have been devoted to mixed ionic liquid and surfactant. The significance and novelty of this research is the investigation of 1-methylimidazolium trinitrophenoxide ([MIm][TNP]) as ionic liquid effects on SDS corrosion behavior.

The inhibition effect of [MIm][TNP], SDS and their mixtures on mild steel surface in 2 M hydrochloric acid (HCl) solution was examined by electrochemical impedance spectroscopy, potentiodynamic polarization (PDP), scanning electron microscopy (SEM), atomic force microscopy and quantum chemical calculations as well as dynamic light scattering (DLS) and surface tension measurements to discuss surface properties of studied solutions.

Based on the results, ionic liquid/SDS mixtures significantly indicated better inhibition properties than pure surfactant solution. PDP curves indicated that the studied compounds act as mixed-type of inhibitors. The critical micelle concentration, surface properties and particle sizes were investigated from the surface tension measurements and DLS results.

Adsorption of the inhibitors on the steel surface obeyed the Villamil adsorption model. SEM was used for surface analysis and verified the inhibition efficiency of mixed IL/SDS system. Quantum chemical calculations were performed using density functional theory, and a good relationship between experimental and theoretical data has been obtained.

The purpose of the present work is to propose a wavelet method for the numerical solutions of Caputo–Hadamard fractional differential equations on any arbitrary interval.

The author has modified the CAS wavelets (mCAS) and utilized it for the solution of Caputo–Hadamard fractional linear/nonlinear initial and boundary value problems. The author has derived and constructed the new operational matrices for the mCAS wavelets. Furthermore, The author has also proposed a method which is the combination of mCAS wavelets and quasilinearization technique for the solution of nonlinear Caputo–Hadamard fractional differential equations.

The author has proved the orthonormality of the mCAS wavelets. The author has constructed the mCAS wavelets matrix, mCAS wavelets operational matrix of Hadamard fractional integration of arbitrary order and mCAS wavelets operational matrix of Hadamard fractional integration for Caputo–Hadamard fractional boundary value problems. These operational matrices are used to make the calculations fast. Furthermore, the author works out on the error analysis for the method. The author presented the procedure of implementation for both Caputo–Hadamard fractional initial and boundary value problems. Numerical simulation is provided to illustrate the reliability and accuracy of the method.

Many scientist, physician and engineers can take the benefit of the presented method for the simulation of their linear/nonlinear Caputo–Hadamard fractional differential models. To the best of the author’s knowledge, the present work has never been proposed and implemented for linear/nonlinear Caputo–Hadamard fractional differential equations.

The purpose of this paper is to study the steady laminar magnetohydrodynamics (MHD) flow of a magnesium oxide-silver/water hybrid nanofluid along a horizontal slim needle with thermal radiation by considering dual solutions.

It is assumed that the needle can move in the same or opposite direction of the free stream. Also the solid phase and fluid phase are in thermal equilibrium. The basic partial differential equations become dimensionless using a similarity transformation method. Moreover, problem coding is accomplished using the finite difference method. The emerging parameters are nanoparticles mass (0–40 gr), base fluid mass (100 gr), needle’s size (0.001–0.2), magnetic field parameter, velocity ratio parameter, radiation parameter and Prandtl number (6.2).

With help of the stability analysis, it is shown that always the first solutions are physically stable. Results indicate that the magnetic parameter and the second nanoparticle’s mass limit the range of the velocity ratio parameter for which the solution exists. Besides, the magnetic parameter leads to decrease of quantities of engineering interest, i.e. skin friction coefficient and local Nusselt number.

To the best of the authors’ knowledge, no one has ever attempted to study the present problem through a mass-based model for hybrid nanofluid. Moreover, the dual solutions for the problem are new. Indeed, the results of this paper are purely original and the numerical achievements were never published up to now. Finally, the authors expect that the present investigation would be useful in hot-wire anemometer or shielded thermocouple for measuring the velocity of the wind, etc.

The purpose of this paper is to investigate analytically the steady general three-dimensional stagnation-point flow of an aqueous titania-copper hybrid nanofluid past a circular cylinder that has a sinusoidal radius variation.

First, the analytic modeling of hybrid nanofluid is presented, and using appropriate similarity variables, the governing equations are transformed into nonlinear ordinary differential equations in the dimensionless stream function, which is solved by the well-known function bvp4c from MATLAB.

The current solution demonstrates good agreement with those of the previously published studies in the special cases of regular fluid and nanofluids. Graphical results are presented to investigate the influences of the titania and copper nanoparticle volume fractions and also the nodal/saddle indicative parameter on flow and heat transfer characteristics. Here, the thermal characteristics of hybrid nanofluid are found to be higher in comparison to the base fluid and fluid containing single nanoparticles. An important point to note is that the developed model can be used with great confidence to study the flow and heat transfer of hybrid nanofluids.

Analytic modeling of hybrid nanofluid is the important originality of present study. Hybrid nanofluids are potential fluids that offer better heat transfer performance and thermophysical properties than convectional heat transfer fluids (oil, water and ethylene glycol) and nanofluids with single nanoparticles. In this investigation, titania (TiO₂, 50 nm), copper (Cu, 20 nm) and the hybrid of these two are separately dispersed into the water as the base fluid and analyzed.

The purpose of the present work is to introduce a wavelet method for the solution of linear and nonlinear psi-Caputo fractional initial and boundary value problem.

The authors have introduced the new generalized operational matrices for the psi-CAS (Cosine and Sine) wavelets, and these matrices are successfully utilized for the solution of linear and nonlinear psi-Caputo fractional initial and boundary value problem. For the nonlinear problems, the authors merge the present method with the quasilinearization technique.

The authors have drived the orthogonality condition for the psi-CAS wavelets. The authors have derived and constructed the psi-CAS wavelets matrix, psi-CAS wavelets operational matrix of psi-fractional order integral and psi-CAS wavelets operational matrix of psi-fractional order integration for psi-fractional boundary value problem. These matrices are successfully utilized for the solutions of psi-Caputo fractional differential equations. The purpose of these operational matrices is to make the calculations faster. Furthermore, the authors have derived the convergence analysis of the method. The procedure of implementation for the proposed method is also given. For the accuracy and applicability of the method, the authors implemented the method on some linear and nonlinear psi-Caputo fractional initial and boundary value problems and compare the obtained results with exact solutions.

Since psi-Caputo fractional differential equation is a new and emerging field, many engineers can utilize the present technique for the numerical simulations of their linear/non-linear psi-Caputo fractional differential models. To the best of the authors’ knowledge, the present work has never been introduced and implemented for psi-Caputo fractional differential equations.

In 1979, twenty‐five hundred years of monarchy came to an abrupt end in Iran. Since the establishment of the Islamic Republic, the new regime has weathered internal uprisings, a costly war with Iraq, and international sanctions. It is interesting to learn the extent to which economic adversities faced by Iran have been ideologically driven. In order to assess this phenomenon, this paper utilizes a framework of analysis laid out by the scholarship of Islamic economics. In section I, the paper discusses some economic forces which underlay the 1979 tumult. Subsequently, a discussion of the new economic and social order is given. An assessment is made in the final section.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

Following the COVID-19 outbreak, there are concerns whether economies are becoming farther from equality and competency. While this matters to every economy, it is more crucial for developing ones who already suffer from income inequalities and lack of competency. The purpose of this paper is to address this issue.

This study uses an administrative data from the Iran's Social Security Organization (ISSO) that provides insurance to workers entitled to the Labor Law of Iran. The data contain more than 7,000,000 workers. The authors assess heterogeneous impact of the first wave of the pandemic by firms' size and average payment.

The authors’ estimation results indicate that, following the initiation of the pandemic, the workers whose corresponding firms are smaller, overall, are more prone to the pandemic and are more likely to submit a request for unemployment benefits. However, the relation is neither homogeneous across sectors nor linear among micro-sized firms. Few sectors indicate a positive relationship between size and likelihood of request submission, including cultural activity, shoemaking and clothing sectors. Besides the size, the authors investigate whether pay grades could explain the probability of becoming unemployed after the pandemic. Results show that workers whose corresponding firms pay less are more likely to submit a request. This is robust within different sectors.

The ISSO dataset is not a panel, so the authors cannot employ methods of causal inferences. The authors’ results should be seen as correlation; however, due to exogeneity and sharpness of the pandemic the result infers to some degree of causality. The data does not cover the informal sector, so the estimates are at lower boundary.

Administrative data on unemployment benefits during COVID-19 show that the pandemic interferes with competition by forcing low-paid workers and small firms to exit the market. This is an alarm for the competition in every economy, specially developing ones.

Application environments of wireless sensor networks (WSNs) include heterogeneous nodes with different packet sizes, transmission abilities and tolerable delay times. This study aims to design a reasonable network topology and transmission timing for these heterogeneous nodes to improve the quality of service (QoS) of networks.

In this paper, the authors treat node urgency and data packets as the basis of network clustering and to extend the network lifetime. The flow, energy consumption and residual energy of a node are included in the cluster head election. We also propose a delay evaluation function.

All the nodes in the network are guaranteed to transmit to the sink nodes efficiently by planning the transmission order in each cluster.

The simulation results show that the proposed method can balance node urgency and data packets path planning, which not only extends the lifetime of the network but also decreases network delay and improves the overall efficiency.