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The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.

This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.

Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.

The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.

It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.

This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

The study examined a wide range of proactive supply chain practices to demonstrate a cross-linkage among them and to understand their effects on both practitioners of previous decision-making models, frameworks, strategies and policies. Here, six supply chain practices are empirically evaluated based on 28 constructs to investigate a comprehensive model and confirm the connections for achieving performance and competence. The study presents a conceptual model and examines the influence of many crucial factors, i.e. supply chain collaboration, knowledge, information sharing, green human resources (GHR) management and lean-green (LG) practices on supply chain performance.

Structural equation modeling (SEM) examines the conceptual model and allied relationship. A sample of 175 respondents' data was collected to test the hypothesized relations. A resource based view (RBV) was adopted, and the questionnaires-based survey was conducted on the Indian supply chain professionals to explore the effect of LG and green human resource management (GHRM) practices on supply chain performance.

The study presented five constructs for supply chain capabilities (SCCA), five constructs for supply chain collaboration and integration (SCIN), four constructs for supply chain knowledge and information sharing (SCKI), five constructs for GHR, five constructs for LG practices (LGPR) and four constructs for lean-green SCM (LG-SCM) firm performance to be utilized for validation by the specific industry, company size and operational boundaries for attaining sustainability. The outcome emphasizes that SCCA positively influence GHRM, LG practices and LG supply chain firm performance. However, LG practices do not influence LG-SCM firm performance, particularly in India.

The study exploited multiple practices in a conceptual model to provide a widespread understanding of decision-making to assist in developing a holistic approach based on different practices for attaining organizational sustainability. The study stimulates the cross-pollination of ideas between many supply chain practices to better understand SCCA, SCIN, SCKI, GHRM and LG-SCM under a single roof for retaining organization performance.

Conducting an analysis spanning from 2000 to 2023, this research evaluates the effectiveness of real estate assets in hedging against global and energy inflation, benchmarked against other compelling investment options such as oil, gold, silver and stocks.

This study employs the wavelet quantile correlation (WQC) methodology. The latter sheds light on dynamic market interactions by scrutinising dependency structures across multiple time scales and also by capturing tail dependence. The adaptability of the wavelet transform, across a spectrum of frequencies, emerges as an indispensable tool for studying time series while also unravelling relationships among variables across diverse quantiles.

The findings reveal that the response to inflationary pressures is contingent upon the asset class, investment horizon and type of inflation under consideration. While precious metals demonstrate effectiveness over short-term horizons, French-listed real estate exhibits compelling inflation-hedging characteristics as the investment horizon extends. Oil emerges as an unequivocal hedge against both global and energy inflation.

To counteract the effects of inflation, investors and households may feel compelled to refine their investment strategies, opting to bolster their portfolios with instruments proven to serve as reliable safeguards against inflation, as indicated by this study.

In conjunction with a surge in inflationary pressures, this study delves into the hedging capabilities of assets, exploring their efficacy not only across short- and long-term investment horizons but also within diverse scenarios characterized by fluctuating levels of global and energy-related inflation. To the best of our knowledge, no previous article employed the WQC technique to evaluate the inflation-hedging nexus.

Today, industrial revolutions demands advanced technologies, means, mediums, tactics and so forth for optimizing their operating behavior and opportunities. It is probed that the effectual results can be seized into system by not only developing advance means and technologies, but also capably adapting these developed technologies, their user interface and their utilization at optimum levels. Today, industrial resources need perfect synchronization and optimization for getting elevated results. Accordingly, present study is furnished with the purpose to expose quality-driven insights to march toward excellence by optimizing existing resources by the industrial organizations. The present study evaluates quality attributes of mechanical machineries for seizing performance opportunities and maintaining competitiveness via synchronizing and reconfiguring firm's resources under quality management system.

In the present study, Kano’s integrated approach is implemented for supporting decision rational concerning industrial assets. The integrative Kano–analytic hierarchy process (AHP) approach is used to reflect the relative importance of quality attributes. Kano and AHP tactics are integrated to define global relative weight and their computational medium is adapted along with ratio analysis, reference point theory and TOPSIS technique for understanding robust decision. The study described an interesting idea for underpinning quality attributes for benchmarking system substitutes. A machine tool selection case is discussed to disclose the significant aspect of decision-making and its virtual qualities.

The decision executives can realize massive benefits by streaming quality data, advanced information, technological advancements, optimum analysis and by identifying quality measures and disruptions for gaining performance deeds. The study determined quality measures for benchmarking machine tool substitute for industrial applications. Momentous machine alternatives are evaluated by means of technical structure, dominance theory and comparative analysis for supporting decision-making of industrial assets based on optimization and synchronization.

The study linked financial, managerial and production resources under sole platform to present a technical structure that may assist in improving the performance of the manufacturing firms. The study provides a decision support mechanism to assist in reviewing the momentous resources to imitate a higher level of productive strength toward the manufacturing firms. The study endeavors its importance toward optimizing resources, which is an evident requirement in industries as the same not only saves money, escalates production, improves profit margins and so forth, but also gratifies the consumption of scarce natural resources.

The study stressed that advance information can be sought from system characteristics in the form of quality measures and attributes, which can be molded for gaining elevated outcomes from existing system characteristics. The same demands decision supports tools and frameworks to utilize data-driven information for benchmarking operations and supply chain activities. The study portrayed an approach for ease of utilizing data-driven information by the decision-makers for demonstrating superior outcomes. The study originally conceptualized multi-attributes appraisement framework associated with subjective cum objective quality measures to evaluate the most significant machine tool choice amongst preferred alternatives.

Security and safety have remained important concerns for mankind since ancient times. In the context of railways, however, the threat perceptions to safety and security have increased significantly lately. In view of this, the Indian Railways requires an effective and efficient security management system. The purpose of this paper is to propose an integrated approach to help develop the Indian railway security system (IRSS) by successively reducing the complexity of the system through a series of studies.

The relevant elements of the complex system of Indian Railways have been identified. The framework in which the elements exist and interact with each other has been clearly established using the interpretive structural modelling (ISM) technique. The output of ISM is further reduced in complexity by having different policy option profiles. A comparison of different option profiles has been done by a multi-criteria decision-making technique, the analytic hierarchy process (AHP), by choosing suitable criteria for comparison.

The following elements need to be pursued as the key objectives for making IRSS: protection of passengers, protection of property, modernisation, manpower enhancement, multi-skilling of staff, latest technology and enhanced legal powers.

The present research can be extended in many important ways. Interpretive structural models for different contextual relationships can be developed and used for formulating and implementing customised security policy. Policy elements and the ISM structure obtained in this research can be utilised for the system dynamic modelling of IRSS. A pilot study can be done to implement the recommendations made in this study.

The ISM model developed can be implemented as a policy tool in enhancing the railway’s security. Some of the policy elements proposed appear to be consistent with the strategic direction being undertaken in the railway security in the country.

Security is an important concern for mankind and social civilisations. The results have significant welfare implications in India and the rest of the world.

The present study is one of the first approaches in a series of studies in railway security in India. The results of this study can be extended to other security scenarios with similar needs.

The purpose of this computational fluid dynamics (CFD)-based study on semicircular rib-roughened equilateral triangular duct is to investigate heat transfer, friction factor and thermohydraulic performance parameter. The analysis is carried out by simulating problem in ANSYS (Fluent). The Reynolds number in the study varies from 4,000 to 24,000. Nusselt number is calculated for different Reynolds number using various turbulent models available in ANSYS (Fluent) for a smooth duct and compared the results with the Dittus–Boelter correlation.

The analysis has been done by solving basic fluid governing equations (continuity, momentum and energy) by using finite volume method (FVM). The semicircular ribs were fabricated on the absorber plate. The constant amount of heat flux is applied on the absorber plate, whereas other two walls are made adiabatic. The semi-implicit method for pressure linked equations (SIMPLE) algorithm is used with pressure–velocity-coupled disretization to estimate the results. The selection of turbulent model has been done on the basis of Nusselt number prediction in the smooth duct.

The renormalization-group k − ε model predicts the Nusselt number more accurately as compared to standard k − ε model, standard k − ω model, shear stress transport (SST) k − ω and realizable k − ε model in the Reynolds number ranges from 4,000 to 24,000 with a ± 5.5% deviation from Dittus–Boelter equation for smooth duct. The maximum thermo-hydraulic performance is observed of the order of 1.7 for arrangement which has a relative roughness height of 0.067 and relative roughness pitch of 7.5 at higher Reynolds Number of 24,000.

Although, many experimental studies are available in the area of rib-roughened ducts, the present study is based on CFD analysis of semicircular rib-roughened equilateral triangular duct and the results are predicted in terms of Nusselt number, friction factor and thermohydraulic performance parameter. Moreover, the predicted result of Nusselt number and friction factor is validated by comparing with Dittus–Boelter correlation and modified Blasius equation, respectively. This advantage made Fluent a powerful tool for analyzing the internal fluid flow through roughened ducts.

The main impetus of the present analysis is to inspect the effect of psychological capital (PsyCap) on entrepreneurial intentions (EI) amongst college students and to test the mediation role of individual expectancy between PsyCap and EI.

The cross-sectional data of the university students enrolled in business management and engineering courses served as the foundation for the quantitative study design. To test the formulated hypothesis, PLS-SEM is utilized for analyzing the structural relationships between dependent and independent variables.

The results indicate that PsyCap significantly impacts the EI of collegiate students pursuing engineering and management courses. However, individual expectancy plays a partial mediating role between the PsyCap and EI of college students.

This research provides an assessment of the effect of PsyCap on the EI of college students with the partial mediating effect of individual expectancy. The findings deliver the enhancement of the EI literature.

The study outcomes are helpful to educators, university administrators, curriculum developers and entrepreneurial policy planners to realize the role of individual expectancies of students’ entrepreneurs on their EI.

The student entrepreneur community recognizes that individual students with stronger levels of entrepreneurial PsyCap having further probability tend to become future entrepreneurs.

The present analysis is original and holds novelty in the form of the mediating role of individual expectancies between PsyCap and EI of students.

Around the world, protecting environment and purchasing green products by the manufacturing firms progressively becomes a popular and important issue. Manufacturers are realizing the importance of producing green products under green practices. This study aims to propose an appraisement platform to evaluate the overall performance index of a firm under green practices. Furthermore, the study also helps in identifying ill-performing areas, which necessarily require future attention to augment green supply chain (GSC) of a firm. A case research is conducted to assess the real-life application by the proposed approach.

The authors used fuzzy performance index to measure the overall performance index of a firm. Beside this, they proposed a degree of similarity approach amalgamated with fuzzy performance importance index to classify the ills and strong indices in GSC extent.

The intermittent assessment of green practices and their metrics in the organizational supply chain management (SCM) is indeed necessary. The present study provides an appraisement module to assess overall GSC fuzzy performance index and also helps in identifying the ill-performing areas which require future augmentation toward successful green implementation.

The exposed research work dealt with chains of subjective indices (measure and their interrelated metrics), which are induced into hierarchical appraisement module. To tackle the uncertainty of indices, the subjective indices are transposed into interval-valued fuzzy number set (IVFNS), as IVFNs are preferred to undertake the uncertainty of GSC indices. The proposed approach is demonstrated with a case research to justify its validity and originality.

Supplier evaluation is a part of logistic management. In the present era, resilient supply chain performance (RSCP) assessment of the vendor enterprise is respected as a hot topic. The purpose of this paper is to enable the managers to map the performance in percentage system and also enabling managers for identifying the weak indices-metrics, which need to be improved up to ideal or standard level and strong indices-metrics.

The authors found two research gaps via a literature survey. The first research gap revealed that the performance of a resilient supplier is computed solely in terms of a fuzzy mathematical scale. The articles are not yet published, which could measure the RSCP in percentage. The second research gap argued about the mitigation of the multi-level hierarchical resilient vendor/supplier evaluation framework for materializing RSCP and identifying weak and strong performing indices-metrics. To compensate the both research gaps, the authors developed a novel fuzzy gain-loss evolutionary computational approach to assess the performance of a firm in percentage. Next, a revised ranking technique coupled with trapezoidal fuzzy set based fuzzy performance importance index is implemented on the framework to seek weak and strong indices-metrics. The performance loss of each metric using the ideal solution concept considering the attitude of decision makers is also revealed.

The authors found the RSC performance of supplier firm 74 per cent, whereas performance loss 26 per cent, while actual performance is compared with standard fuzzy performance index (SFPI). Performance loss 26 per cent can be compensated by improving the performance of weak indices-metrics.

The novelty of the paper is that the authors used the ideal solution concept to compute the SFPI and compare it with actual FPI for evaluating the gain and loss of resilient supplier firm in percentage and identify weak and strong indices so that managers can improve the performance of weak indices. The work possesses the significant for all organizations, as research work enables the managers to map and improve the RSC performance of any vendor firm in future. The presented work considers the case of an automobile parts supplier industry to validate the developed approach.

The purpose of this study is to design a DSS for construction sectors, which can determine the status of the related supplier alternatives, accompanying G-T, SC measures and their interrelated metrics. In today’s era, a supplier is observed as significant among entire agents of green supply chain (SC) management. Presently, it is determined that appraising worth of the supplier under green-traditional (G-T), SCs concerns still require the support of novel algorithmic/decision support systems (DSSs), which could embrace potential decision-making.

The authors have proposed a DSS (consisting of the implementation of multi-level multi-criterion decision-making [ML-MCDM], reference point approach [RPA] and multi-objective optimization on the basis of simple ratio analysis [MOOSRA] methods on constructed MCDM supplier evaluation appraisement module) for measuring the performance score of clay-brick suppliers coming under G-T SCs corresponding to fuzzy and non-fuzzy information. A comparative analysis is conducted among the performance scores against alternatives, obtained by the three methods, i.e. ML-MCDM, RPA and MOOSRA, for robustly making a potential decision.

The presented research offers a DSS toward managers of construction sectors for benchmarking the performance scores against supplier alternatives under G-T SC measures and their interrelated metrics, modeled by fuzzy cum non-fuzzy information.

Presented research work exhibited a DSS that can be used by construction sectors for benchmarking the supplier alternatives in accordance with their performance scores under G-T SCs. The MCDM G-T supplier evaluation appraisement module is constructed pertaining to small-scale clay-brick production units, located in the northern part of India to check the effectiveness of the proposed DSS.