Search results

This study describes a series of experiments investigating the upper hot layer temperature profile in a confined space under different ventilation conditions for porosity-controlled wood crib fires for pre-flashover conditions.

Full-scale compartment (4 m × 4 m × 4 m) experiments were carried out for four-door openings, i.e. 100%, 75%, 50% and 25% of the total vent area (2 m × 1 m) with the wood crib as a fuel load. The temperature of the upper hot smoke layers of the compartment was recorded with the help of four layers of thermocouples for varying vent areas.

The effect of ventilation on the properties, i.e. mass loss rate, enclosure temperature, heat release rate and carbon monoxide (CO) gas concentration, has been measured and analyzed. The effect of ventilation on heat flux and flame temperature has also been studied. Compartment gas temperature has been examined by five wood crib burning stages: Ignition, growth, steady burning, recess and collapse.

Findings demonstrate that the influence of vent openings varies for the burning parameters and upper layer temperature of the compartment. The current results are beneficial in analyzing thermal risks concerning compartment fire and fire safety engineering projects.

The present paper investigated a skim milk powder production plant with genuine human mistake for analyzing its performance in terms of its reliability, availability and maintainability (RAM) indices along with mean time between failure (MTBF) and expected number of failure (ENOF).

In the proposed work, the generalized fuzzy lambda–tau methodology has been used to carry out the analysis of the repairable structure using the improved arithmetic operations for generalized fuzzy numbers by considering the degree of confidence levels.

RAM indices along with MTBF and ENOF are obtained to increase the quality of skim milk powder manufacturing structures of a dairy plant with genuine human-mistake working conditions.

In the present paper, a mathematical model for a complex industrial system based on fuzzy has been developed. Finally, the results are more realistic and comprehensive for the decision-maker for farther application.

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

The purpose of this paper is to investigate the performance of a turbine structure of the oil and gas Egyptian company in terms of reliability, mean time to failure (MTTF), mean time to repair (MTTR) and mean time between failures (MTBF) under fuzzy environment and working criteria. This paper examines the impact of the failure of various components on the complete turbine structure of the oil and gas system.

To overcome the problem of uncertain behavior of available data for various components, the right triangular generalized fuzzy number (RTrGFN) is proposed to be taken into the account to express the uncertainty which attains some tolerance in data. Furthermore, reliability indices are calculated with the help of the Lambda Tau method and the arithmetic operations on right generalized triangular fuzzy numbers (RTrGFN).

This paper explores the reliability of a repairable 3 out of 4 structure of turbines and along with the other parameters namely MTTF, MTTR and MTBF; under a fuzzy environment. Failure rates and repair times are expected to be exponential. The ranking of components of the structure is being found to decide the priority for maintenance.

This paper investigates the performance of the system with different spread/tolerance like 15%, 25% and 50% of crisp data. It helps to predict realistic results in the range value. To enhance the system's performance, the most important item of the system requires greater attention. For this, the authors find the sensitive part by ranking. For ranking, an extended approach has been developed to find the sensitive unit of the system by using the right triangular generalized fuzzy number. This paper explores the most and least sensitive component of the system, which helps the maintenance department to plan the maintenance action.

A collocation technique based on re-defined quintic B-splines over Crank-Nicolson is presented to solve the Fisher's type equation. We take three cases of aforesaid equation. The stability analysis and rate of convergence are also done.

The quintic B-splines are re-defined which are used for space integration. Taylor series expansion is applied for linearization of the nonlinear terms. The discretization of the problem gives up linear system of equations. A Gaussian elimination method is used to solve these systems.

Three examples are taken for analysis. The analysis gives guarantee that the present method provides much better results than previously presented methods in literature. The stability analysis and rate of convergence show that the method is unconditionally stable and quadratic convergent for Fisher's type equation. Moreover, the present method is simple and easy to implement, so it may be considered as an alternative method to solve PDEs.

This work is the original work of authors which is neither published nor submitted anywhere else for publication.

Each individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already strained supply chain in low- and middle-income countries (LMICs) will not be effective enough to vaccinate all the population in stipulated time. The purpose of this paper is to show that there is a need to revolutionize the vaccine supply chain (VSC) by overcoming the challenges of sustainable vaccine distribution.

An integrated lean, agile and green (LAG) framework is proposed to overcome the challenges of the sustainable vaccine supply chain (SVSC). A hybrid best worst method (BWM)–Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS) methodology is designed to analyze the challenges and solutions.

The analysis shows that vaccine wastage is the most critical challenge for SVSC, and the coordination among stakeholders is the most significant solution followed by effective management support.

The result of the analysis can help the health care organizations (HCOs) to manage the VSC. The effective vaccination in stipulated time will help control the further spread of the virus, which will result in the normalcy of business and availability of livelihood for millions of people.

To the best of the author's knowledge, this is the first study to explore sustainability in VSC by considering the environmental and social impact of vaccination. The LAG-based framework is also a new approach in VSC to find the solution for existing challenges.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.

The purpose of this paper is to develop hybrid aluminum metal matrix composite by stir casting process, reinforced with graphite and hard boron carbide particles to enhance the wear resistance. An attempt is made to optimize the wear (weight loss) and coefficient of friction (COF) by considering three factors, i.e. normal load, track diameter and sliding speed which were varied at three different levels.

The effect of graphite and boron carbide on microhardness was studied by adding them in varying percentages. After determining the best combination of hybrid reinforcements, optimization of wear (weight loss) and COF was carried out at various levels of considered factors. Taguchi design of experiments was used using the software “Minitab 16.1”. ANOVA was used to analyze the effect of various parameters on wear and COF. To validate the results, mathematical modeling was carried out in terms of regression equations and results obtained by regression equations.

The results revealed that the lower weight percentage of graphite (3 per cent) and boron carbide (1 per cent) significantly improved microhardness of developed composites. Results of ANOVA revealed that normal load was the main contributing factor for wear and COF. The results obtained by regression equations and confirmatory tests were within the results obtained by ANOVA.

To the best of the author’s knowledge, very less work has been reported on optimization of wear and COF using hybrid reinforcement particles of graphite and boron carbide.

Nation brand building is a complex task as it involves multiple stakeholders. In the context of globalisation, a strong nation brand has important implications not only for attracting foreign businesses and tourists but also for the brands originating from that country. This study examines the role of nation brand experience in enhancing a nation's global reputation.

The relationships between nation brand experience, nation brand love, nation brand loyalty and positive word of mouth (WOM) were examined by analysing 334 survey responses collected from foreign tourists in India. Structural equation analysis was carried out as part of the analysis.

The results confirmed that international visitors' sensory and affective nation brand experiences significantly enhance brand love for a nation, leading to national brand loyalty and positive WOM among them. The findings are expected to help practitioners understand consumer buying behavior related to nation brand and develop innovative strategies for improving brand values.

Understanding a developing nation brand experience and how it converts to brand loyalty through the mediation effect of nation brand love from the lens of foreign tourists is the novelty of this study.

The manufacturing industry is presently experiencing technological disruption on a global scale. Consequently, to tackle such disruption, firms are identifying a volatile, uncertain, complex and ambiguous (VUCA) scenario and seeking ways to counter it. Accordingly, this paper aims to investigate the employee performance through assessing organizational citizenship behaviour (OCB) among the shop floor employees of the fast-moving consumer goods (FMCG) industry where a high-performance work system (HPWS) has been implemented.

A descriptive research design was used in the study, and 395 shop floor employees working in leading multinational firms, with a minimum global turnover of US$1bn, were interviewed. These manufacturing firms were located in three industrial clusters in the northern part of India.

The results indicate that HPWS influences OCB. Most of the dimensions of HPWS and OCB were found to be positively associated. The findings also disprove the labour process theory in the context of the study.

The findings report a broad view of the relationship between HPWS and OCB in the Indian manufacturing context. The study offers the practical insights that HPWS is a universally accepted framework and that organizations should focus on the effective implementation of HPWS in a VUCA scenario, which is in line with past studies. The study also provides future directions for research.

This paper has established the relationship between HPWS and OCB in the manufacturing sector, especially for shop floor employees.