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This paper proposes an integrated methodology for process design to guide decision making under uncertainty by combining life cycle assessment (LCA) with multi‐criteria decision‐making tools.

Cleaner and greener technologies for process and product selection and design have gained popularity in recent years. The LCA is a systematic approach that enables selection of cleaner and greener products and processes. Recently, significant progress has been made for the use of LCA for product/process evaluation and selection. However, its use in process design and environmental decision making has not been fully exploited. The proposed methodology GreenPro‐I is a systematic approach to estimate environmental risks/impacts associated with life cycle of products, processes and services. It evaluates environmental burdens by quantifying energy and materials used and waste released into the environment. It identifies and evaluates opportunities, which affect environmental improvements. The assessment includes the extraction/excavation and processing of raw materials, manufacturing, transportation and distribution, use, recycle, and final disposal.

GreenPro‐I overcomes many of the problems faced in the conventional approaches and establishes a link between the environmental risks/impacts, cost, and technical feasibility of processes.

GreenPro‐I provides a comprehensive decision‐making tool for designers, regulatory agencies, business organizations and other stakeholders.

Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues.

In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization.

A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed.

A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.

Implementation of a sustainability paradigm demands new choices and innovative ways of thinking. The main objective of this paper is to provide a meaningful sustainability assessment tool for make informed decisions, which is applied to higher education institutions (HEIs).

The objective is achieved by developing a quantitative tool for sustainability assessment using a driving force‐pressure‐state‐exposure‐effect‐action (DPSEEA) framework. The DPSEEA framework considers environmental, social, economic, and educational performance as main dimensions of sustainability. The proposed model is called DPSEEA‐Sustainability index Model (D‐SiM). The D‐SiM is a causality‐based model in which the sustainability index (SI) is an outcome of nonlinear effects of sustainability indicators in various stages of DPSEEA. To have an improved understanding of input factors (driving forces) and their impact on sustainability, a simplified empirical model is developed and applied to HEIs to determine the percent contribution of various driving forces on sustainability.

The study reveals that economic development, social equity, and education in sustainability are the major drivers for achieving sustainability in HEI, while health and safety issues, energy requirements, institutional enhancement, and international research and development trends are the less significant driving forces.

The indicators connected in DPSEEA framework through causal relationships lead to the quantitative assessment of sustainability, which provides a unique approach for informed decision making.

This paper aims to reviewed analytical methodologies, i.e. lumped parameter magnetic equivalent circuit (LPMEC), magnetic co-energy (MCE), Laplace equations (LE), Maxwell stress tensor (MST) method and sub-domain modelling for design of segmented PM(SPM) consequent pole flux switching machine (SPMCPFSM). Electric machines, especially flux switching machines (FSMs), are accurately modeled using numerical-based finite element analysis (FEA) tools; however, despite of expensive hardware setup, repeated iterative process, complex stator design and permanent magnet (PM) non-linear behavior increases computational time and complexity.

This paper reviews various alternate analytical methodologies for electromagnetic performance calculation. In above-mentioned analytical methodologies, no-load phase flux linkage is performed using LPMEC, magnetic co-energy for cogging torque, LE for magnetic flux density (MFD) components, i.e. radial and tangential and MST for instantaneous torque. Sub-domain model solves electromagnetic performance, i.e. MFD and torque behaviour.

The reviewed analytical methodologies are validated with globally accepted FEA using JMAG Commercial FEA Package v. 18.1 which shows good agreement with accuracy. In comparison of analytical methodologies, analysis reveals that sub-domain model not only get rid of multiples techniques for validation purpose but also provide better results by accounting influence of all machine parts which helps to reduce computational complexity, computational time and drive storage with overall accuracy of ∼99%. Furthermore, authors are confident to recommend sub-domain model for initial design stage of SPMCPFSM when higher accuracy and low computational cost are primal requirements.

The model is developed for high-speed brushless AC applications.

The SPMCPFSM enhances electromagnetic performance owing to segmented PMs configuration which makes it different than conventional designs. Moreover, developed analytical methodologies for SPMCPFSM reduce computational time compared with that of FEA.

The shadow economy (SE) has been a serious issue with varied dimensions in all countries that significantly affect economic growth. Therefore, all countries have made an effort to tackle the SE by pursuing several measures. This study aims to investigate the impact of financial markets (stock and bond) in reducing the SE while considering the role of country risk (political, economic and financial) in N-11 countries.

The study employed first-generation methodological techniques, including a unit root test to identify stationarity in the series, a panel cointegration test and panel autoregressive distributive lag (ARDL) to estimate long-run and short-run relationships. Finally, the Granger causality is applied to determine the direction of the causal relationship.

The study explored that country risk factors are crucial in reducing the size of the SE. Moreover, the significant moderating role of country risk factors in the financial market development and SE nexus suggests that by controlling the country's risk, financial market development can negatively affect the SE.

Due to the availability of data, the study used data, ranging from 1995 to 2015, because the tax burden data is available from 1995 while the maximum data for the SE is available till 2015, using Medina and Schneider's (2019) data estimates for the SE.

The previous studies have focused explicitly on the role of financial institutions' development in the SE. To the best of the author's knowledge, no previous study is attempted to investigate the role of financial markets (bonds and stock) in the size of the SE. Furthermore, previous studies have ignored the important role of country risk factors in the size of the SE. This study investigates the impact of country risk on the SE and the moderating role of country risk in the development of financial markets and the SE nexus.

The purpose of this paper is to analyze electromagnetic performance and develop an analytical approach to find the suitable coil combination and no-load flux linkage of the proposed hybrid excited consequent pole flux switching machine (HECPFSM) while minimizing the drive storage and computational time which is the main problem in finite element analysis (FEA) tools.

First, a new HECPFSM based on conventional consequent pole flux switching permanent machine (FSPM) is proposed, and lumped parameter magnetic network model (LPMNM) is developed for the initial analysis like coil combination and no-load flux linkage. In LPMNM, all the parts of one-third machine are modeled which helps in reduction of drive storage, computational complexity and computational time without affecting the accuracy. Second, self and mutual inductance are calculated in the stator, and dq-axis inductance is calculated using park transformation in the rotor of the proposed machine. Furthermore, on-load performance analysis, like average torque, torque density and efficiency, is done by FEA.

The developed LPMNM is validated by FEA via JMAG v. 19.1. The results obtained show good agreement with an accuracy of 96.89%.

The proposed HECPFSM is developed for high-speed brushless AC applications like electric vehicle (EV)/hybrid electric vehicle (HEV).

The proposed HECPFSM offers better flux regulation capability with enhanced electromagnetic performance as compared to conventional consequent pole FSPM. Moreover, the developed LPMNM reduces drive storage and computational time by modeling one-third of the machine.

This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

Nonlinear mixed-convective entropy optimized the flow of hyperbolic-tangent nanofluid (HTN) with magnetohydrodynamics (MHD) process is considered over a vertical slendering surface. The impression of activation energy is incorporated in the modeling with the significance of nonlinear radiation, dissipative-function, heat generation/consumption connection and Joule heating. Research in this area has practical applications in the design of efficient heat exchangers, thermal management systems or nanomaterial-based devices.

Suitable set of variables is introduced to transform the PDEs (Partial differential equations) system into required ODEs (Ordinary differential equations) system. The transformed ODEs system is then solved numerically via finite difference method. Graphical artworks are made to predict the control of applicable transport parameters on surface entropy, Bejan number, Sherwood number, skin-friction, Nusselt number, temperature, velocity and concentration fields.

It is noticed from present numerical examination that Bejan number aggravates for improved estimations of concentration-difference parameter a_2, Eckert number E_c, thermal ratio parameter ?_w and radiation parameter R_d, whereas surface entropy condenses for flow performance index n, temperature-difference parameter a_1, thermodiffusion parameter N_t and mixed convection parameter ?. Sherwood number is enriched with the amplification of pedesis-motion parameter N_b, while opposite development is perceived for thermodiffusion parameter. Lastly, outcomes are matched with formerly published data to authenticate the present numerical investigation.

To the best of the authors' knowledge, no investigation has been reported yet that explains the entropic behavior with activation energy in the flowing of hyperbolic-tangent mixed-convective nanomaterial due to a vertical slendering surface.

This paper aims to develop an analytical approach to validate the finite element analysis (FEA) results. FEA itself is a powerful tool to evaluate the performance of electrical machines but takes more time and requires more drive storage. To overcome this issue, subdomain modeling (SDM) is used for the proposed machine.

SDM is developed to validate the electromagnetic performance of a new linear hybrid excited flux switching machine (LHEFSM) with ferrite magnets. In SDM, the problem is divided into different physical regions called subdomains. Maxwell's governing equation is solved analytically for each region, where the magnetic flux density (MFD) is generated. From the generated MFD, x and y components are calculated, which are then used to find the useful force along the x-axis.

FEA validates the developed SDM via JMAG v. 20.1. The results obtained show excellent agreement with an accuracy of 95.13%.

The proposed LHEFSM is developed for long stroke applications like electric trains.

The proposed LHEFSM uses low-cost ferrite magnets with DC excitation, which offers better flux regulation capability with improved electromagnetic performance. Moreover, the developed SDM reduces drive storage and computational time by modeling different parts of the machine.

This paper aims to address the flow features of Ree–Eyring fluid between two rotating disks subject to the magnetic field. Heat transfer features are discussed through viscous dissipation and nonlinear thermal radiation. Impact of thermophoresis and Brownian movement are elaborated. Physical characteristics of entropy generation optimization in nanofluid with homogeneous and heterogeneous chemical reaction are discussed.

The nonlinear system leads to ordinary one through the implementation of adequate transformation and then tackled analytically for a convergent series solution by homotopy analysis method.

The prime objective of the present research has been given to investigate entropy generation in Ree–Eyring fluid flow between two rotating disks subjected to the magnetic field. Vital features, namely, Brownian motion and thermophoresis have been addressed. Total entropy rate is computed using the second law of thermodynamics.

No such work yet exists in the literature.