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This study proposes a hybrid multiple criteria decision making (MCDM) methodology for evaluating the performance of the Indian railway stations (IRS). Since the customers are heterogeneous and their requirements are often imprecise, the evaluation process is a critical step for prioritizing the IRS. To improve the existing approaches, an efficient evaluation technique has been proposed by integrating rough numbers, analytic hierarchy process (AHP) and multi-attribute border approximation area comparison (MABAC) methods in rough environment. The relative criteria weights based on their preferences given by experts is determined by rough AHP whereas evaluation of the alternatives based on these criteria are done by the modified rough MABAC method. A case study of prioritizing different railway stations in India is provided to demonstrate the efficiency and applicability of the proposed method. Among different criteria “proactively” is observed to be the most important criteria in our analysis, followed by ‘Railfanning’ and ‘DMO’ is found to be the best among the forty IRS in this study. Finally, a comparative analysis and validity testing of the proposed method are elaborated and the methodology provides a standard to select IRS on the basis of different criteria.

Metaheuristic approaches with significant improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare performance of the four selected algorithms from "classical" (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and "new generation" (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems. SOS and KH algorithm successfully solved most of all the selected problems by achieving the best solution in minimum execution time. On the other hand, DE, and PSO also effectively attained the optimal solution which were very close to the best one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, "new generation" algorithm exhibited the most promising result and great potential than the "classical" one. This study gives some insights to use SOS and KH as best performing algorithm to the novice user who can easily get lost by the plethora of large number of optimization algorithms. [ABSTRACT FROM AUTHOR]

The objective of the study was to assess the optimum frying condition of fish considering the multiple perspectives (retention of nutritional quality indices [NQI], reduction of preparation time, and improvement of health benefit) to satisfy consumer‐preferred sensory attributes by controlling the most impactful process variables (temperature, time, and oil amount). The multi‐criteria decision‐making (MCDM) approach is appropriate to handle the numerous conflicting criteria and numerous multiple objectives. First, an artificial neural network (ANN) model was developed to build a non‐linear correlation between the cooking process parameters and NQI by an automatic exhaustive search of all available algorithms and activation functions. All the NQI are conflicting in nature. Therefore, the ANN‐based multi‐objective genetic algorithm was implemented to obtain the Pareto optimal solutions to improve all NQI simultaneously. Five optimised conditions were selected amongst the Pareto optimal solutions, satisfying the above‐mentioned multiple criteria. Finally, a well‐known MCDM approach, the analytical hierarchy process (AHP), was applied for sensory analysis to evaluate the overall acceptance of the optimised conditions based on the relative importance of consumers' general sensory modalities (flavour, colour & appearance, and taste). Furthermore, the following condition (140.01°C, 7.62 min, 47.87 ml oil/kg of fish) was selected as the most accepted in terms of all quality attributes that may be implemented as the standard condition in domestic and industrial purposes. [ABSTRACT FROM AUTHOR]

Nano-biotechnology deals with the role of biological species in the field of nanotechnology and has gained significant attention in the last few decades. Nano-sized materials have superior physical, chemical, optical, electrical and mechanical properties in comparison with the bulk materials. A number of techniques have been employed so far to synthesize nano-structured materials, and among these, bio-inspired methods are found to be economically viable and environmentally sustainable. Titanium dioxide (TiO2) is one of the most abundant and nontoxic transition metal oxides with high thermal stability, good oxidation and corrosion resistance, good hydrophobicity and non-wettability. All these properties along with a wide band gap make TiO2 an important material for various applications in the fields of solar cells, sensors, batteries, and supercapacitors. In this review work, a comprehensive study has been carried out on the synthesis of TiO2 nanoparticles (NPs) using different biological extracts and applications of TiO2 NPs in the design and development of various electronic devices. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel Multiple Criteria Decision Making methodology for assessing and prioritizing medical tourism destinations under uncertainty. A systematic evaluation and assessment approach is proposed by incorporating analytic hierarchy process and multi‐attributive border approximation area comparison methods in the rough environment. Rough number is used to aggregate individual judgements of decision makers and express their true perception to handle vagueness without any prior information. Rough analytic hierarchy process analyses the relative importance of criteria based on their preferences given by experts, whereas rough multi‐attributive border approximation area comparison evaluates the alternative sites based on the criteria weights. A case study of prioritizing different sites (cities) in India for medical tourism services is shown to demonstrate the applicability of the proposed method. Among different criteria “quality of infrastructure of healthcare institutions” is observed to be the most important criteria in our analysis, followed by “supply of skilled human resources and new job creations” and “Chennai” is found to be the best medical tourism site in India. Finally, a comparative analysis and validity testing of the proposed method are elaborated, and the methodology provides a standard for select medical tourism sites on the basis of different criteria. [ABSTRACT FROM AUTHOR]