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This study aims to use a machine learning (ML) model for the prediction of traction coefficient and asperity load ratio for different surface topographies of non-conformal rough contacts.

The input data set for the ML model is generated using a mixed-lubrication model. Surface topography parameters (skewness, kurtosis and pattern ratio), rolling speed and hardness are used as input features in the multi-layer perceptron (MLP) model. The hyperparameter tuning and fivefold cross-validation are also performed to minimize the overfitting.

From the results, it is shown that the MLP model shows excellent accuracy (R² > 90%) on the test data set for making the prediction of mixed lubrication parameters. It is also observed that engineered rough surfaces with high negative skewness, low kurtosis and isotropic surface patterns exhibit a significant low traction coefficient. It is also concluded that the MLP model gives better accuracy in comparison to the random forest regression model based on the training and testing data sets.

Mixed lubrication parameters are predicted by developing a regression-based MLP model. The machine learning model is trained using several topography parameters, which are vital in the mixed-EHL regime because of the lack of regression-fit expressions in previous works. The accuracy of MLP with random forest models is also compared.

The purpose of this paper is to investigate the performance of bio-based lubricants (BBL), namely, palm mid-olein (PMO) enriched with an antioxidant agent, tertiary-butylhydroquinone (TBHQ) and a viscosity improver, ethylene-vinyl acetate (EVA), in journal bearing (JB) applications.

Samples of the BBL were prepared by blending it with TBHQ and EVA at various blending ratios. The oxidative stability (OS) and viscosity of the BBL samples were examined using differential scanning calorimetry and a viscometer, respectively. Meanwhile, their performance in JB applications was evaluated through the use of a JB test rig with a 0.5 length-to-diameter ratio at various operating conditions.

It was found that the combination of PMO + TBHQ + EVA demonstrated a superior oil film pressure and load-carrying capacity, resulting in a reduced friction coefficient and a smaller attitude angle compared to the use of only PMO or VG68. However, it was observed that the addition of TBHQ and EVA to the PMO did not have a significant impact on the minimum oil film thickness.

The results would be quite useful for researchers generally and designers of bearings in particular.

This study used PMO as the base stock, and its compatibility with TBHQ and EVA was investigated in terms of its OS and viscosity. The performance of this treated BBL was evaluated in a hydrodynamic JB.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0363/

Erosion and abrasion are the prominent wear mechanisms reducing the lifetime of machine components. Both wear mechanisms are playing a role meanwhile, generating a synergy, leading to a material removal on the target. The purpose of study is to create a mathematical expression for erosive abrasive wear.

Many factors such as environmental cases and material character have an influence in erosive abrasive wear. In the work, changes in abrasive size and material hardness have been analyzed. As an abrasive particle, quartz sand has been used. All tests have been done in 20 wt.% slurry. Heat treatment has been applied to different steel specimens (steel grades C15, St 37 and Ck45) to change hardness value, which ranged from 185 to 880 Vickers hardness number.

After the four-hour test, it is determined that by an increase in abrasive size and decrease in material hardness, wear rate increases. Worn surfaces of the targets have been examined to figure out the wear mechanisms at different conditions under scanning electron microscopy. The results indicate that by an increase in material hardness, the number and diameter of micro-craters on the worn surfaces decrease. The diameters of micro-craters have been about 3–8 µm in hard materials and about 120–140 µm in soft materials.

It is determined that by an increase in abrasive size and decrease in material hardness, wear rate increases. The results indicate that by an increase in material hardness, the number and diameter of micro-craters on the worn surfaces decrease.

The study enables to indicate the dominant factor in worn steel used in mechanical components.

After analyzing the test results, a novel mathematical expression, considering both abrasive size and material hardness, has been developed.

The objective of the present study is to examine the impact of corporate characteristics on human resource disclosures in Indian corporate sector.

The study investigates the annual reports of 336 Indian listed companies of NSE-500 Index. The data are collected for the latest time period which contains eight years (FY 2012–13 to 2019–2020). The data of independent variables (company characteristics) have collected from annual reports and CMIE ProwessIQ Database of the Indian listed companies. The data of human resource dissclosure index (HRDI) is collected form annual reports using content analysis approach. For analysis purpose, descriptive statistics, Pearson's correlation matrix, Two-way Least Square Dummy Variable (LSDV) regression model have been used.

The outcomes show that net sales, market capitalisation, ROTA, return on equity, quick ratio, PAR have significant positive and age, profit after tax, current ratio have significant negative effect on HRDI. On the contrary, debt-equity ratio, earnings per share, type of auditor, listing status have insignificant positive and net fixed assets, promoter's holding have insignificant negative effect on HR disclosures of the selected Indian listed companies.

The HRDI constructed in the present study helps the Institute of Chartered Accountants of India (ICAI) and other regulatory bodies to make some standards regarding voluntary HR disclosure practices in Indian corporate sector.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

Consistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods with intuitionistic multiplicative preference relations (IMPRs), a new GDM method with complete IMPRs (CIMPRs) and incomplete IMPRs (ICIMPRs) is proposed in this paper.

A mathematically programming model is constructed to judge the consistency of CIMPRs. For the unacceptably consistent CIMPRs, a consistency-driven optimization model is constructed to improve the consistency level. Meanwhile, a consistency-driven optimization model is constructed to supplement the missing values and improve the consistency level of the ICIMPRs. As to GDM with CIMPRs, first, a mathematically programming model is built to obtain the experts' weights, after that a consensus-driven optimization model is constructed to improve the consensus level of CIMPRs, and finally, the group priority weights of alternatives are obtained by an intuitionistic fuzzy programming model.

The case analysis of the international exchange doctoral student selection problem shows the effectiveness and applicability of this GDM method with CIMPRs and ICIMPRs.

First, a novel consistency definition of CIMPRs is presented. Then, a consistency-driven optimization model is constructed, which supplements the missing values and improves the consistency level of ICIMPRs simultaneously. Therefore, this model greatly improves the efficiency of consistency improving. Experts' weights determination method considering the subjective and objective information is proposed. The priority weights of alternatives are determined by an intuitionistic fuzzy (IF) programming model considering the risk preference of experts, so the method determining priority weights is more flexible and agile. Based on the above theoretical basis, a new GDM method with CIMPRs and ICIMPRs is proposed in this paper.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.

The study aims to propose a conceptual Bhartiya (Indian) model of workplace spirituality (WPS) in non-profit organisations (NPOs) in the context of burnout and resilience by synthesising the concepts of the east and the west. The researchers have kept an open approach by exploring various dimensions of WPS by reviewing the extant literature of both the east and the west. The researchers delved into Bhartiya (Indian) scriptures to identify the concepts that have similarity with the dimensions of WPS so that it may further assist in facilitating those dimensions in NPOs. Furthermore, to propose a conceptual Bhartiya model for NPOs, the researchers synthesised the literature pool of Bhartiya studies on WPS. They examined how WPS decreases burnout and leads to resilience. The study’s findings reveal that concepts from Bhartiya scriptures such as Karm Yog (Nishkam Karm, self-abnegation, swadharm), parasparam bhavayantaha, loksangrah, daivi sampat and kritagyata are instrumental in facilitating the constructs of WPS. Meaningful work is facilitated through karm yog; sense of community is facilitated through parasparam bhavayantaha and loksangrah; and alignment with organisational values is facilitated through daivi sampat and kritagyata. The findings further suggest that WPS is an antidote to burnout and an enabler of resilience.

Rising energy demand and the quest for achieving climate change targets have been pushing emerging markets like India to bolster the natural gas share in their energy mix. The country has set an aggressive target of increasing natural gas share in the energy mix to 15% by 2030. The purpose of this study is to acknowledge the need for adopting and developing strategies for natural gas business market development to ensure a reliable supply at an affordable price. Hence, this study explores the natural gas market business development strategies and assesses them through cause/effect analysis.

This study proposed an integrated framework based on the Grey concept and Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to assess and determine the interdependence among the natural gas business market development strategies by cause-and-effect group analysis. The application of Grey theory reduced the uncertainty and subjectivity involved in the decision-making process. Later, sensitivity analysis is also performed to check the robustness of the framework.

The natural gas business market development strategies are identified through a systematic literature search and contributions from industry experts. The findings of this study highlight the importance of developing pipeline and storage infrastructure facilities, ensuring supply security through long-term imports and overseas investment, implementing free-market-based pricing, simplification and standardization of regulatory processes at state and national levels, etc., for the development of the natural gas market development in India.

This study acknowledges the natural gas market development strategies and evaluated them into cause-and-effect groups which are limited to Indian context. All evaluations in the Grey-based DEMATEL method were made in this study based on the decision team inputs which limits the generalization to other geographies. Moreover, the opinions of the experts can be subjective and differ. The selection of the experts is done through non-probability sampling process.

This study could support the government and decision-makers in formulating the appropriate strategies to develop the domestic natural gas market. The cause-and-effect relationships are helpful for the companies, management, government, regulators and other stakeholders to understand the criticality of the causal strategies that must be implemented for developing the favorable natural gas business market scenario.

This study explores and evaluates the strategies that successfully bolster the natural gas business demand in India using Grey-based DEMATEL framework. By focusing on those critical strategies, relevant stakeholders would ensure a reliable natural gas supply at affordable prices.