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The purpose of this paper is to estimate the multicomponent reliability by assuming the unit-Gompertz (UG) distribution. Both stress and strength are assumed to have an UG distribution with common scale parameter.

The reliability of a multicomponent stress–strength system is obtained by the maximum likelihood (MLE) and Bayesian method of estimation. Bayes estimates of system reliability are obtained by using Lindley’s approximation and Metropolis–Hastings (M–H) algorithm methods when all the parameters are unknown. The highest posterior density credible interval is obtained by using M–H algorithm method. Besides, uniformly minimum variance unbiased estimator and exact Bayes estimates of system reliability have been obtained when the common scale parameter is known and the results are compared for both small and large samples.

Based on the simulation results, the authors observe that Bayes method provides better estimation results as compared to MLE. Proposed asymptotic and HPD intervals show satisfactory coverage probabilities. However, average length of HPD intervals tends to remain shorter than the corresponding asymptotic interval. Overall the authors have observed that better estimates of the reliability may be achieved when the common scale parameter is known.

Most of the lifetime distributions used in reliability analysis, such as exponential, Lindley, gamma, lognormal, Weibull and Chen, only exhibit constant, monotonically increasing, decreasing and bathtub-shaped hazard rates. However, in many applications in reliability and survival analysis, the most realistic hazard rates are upside-down bathtub and bathtub-shaped, which are found in the unit-Gompertz distribution. Furthermore, when reliability is measured as percentage or ratio, it is important to have models defined on the unit interval in order to have plausible results. Therefore, the authors have studied the multicomponent stress–strength reliability under the unit-Gompertz distribution by comparing the MLEs, Bayes estimators and UMVUEs.

The purpose of this article is to derive inference for multicomponent reliability where stress-strength variables follow unit generalized Rayleigh (GR) distributions with common scale parameter.

The authors derive inference for the unknown parametric function using classical and Bayesian approaches. In sequel, (weighted) least square (LS) and maximum product of spacing methods are used to estimate the reliability. Bootstrapping is also considered for this purpose. Bayesian inference is derived under gamma prior distributions. In consequence credible intervals are constructed. For the known common scale, unbiased estimator is obtained and is compared with the corresponding exact Bayes estimate.

Different point and interval estimators of the reliability are examined using Monte Carlo simulations for different sample sizes. In summary, the authors observe that Bayes estimators obtained using gamma prior distributions perform well compared to the other studied estimators. The average length (AL) of highest posterior density (HPD) interval remains shorter than other proposed intervals. Further coverage probabilities of all the intervals are reasonably satisfactory. A data analysis is also presented in support of studied estimation methods. It is noted that proposed methods work good for the considered estimation problem.

In the literature various probability distributions which are often analyzed in life test studies are mostly unbounded in nature, that is, their support of positive probabilities lie in infinite interval. This class of distributions includes generalized exponential, Burr family, gamma, lognormal and Weibull models, among others. In many situations the authors need to analyze data which lie in bounded interval like average height of individual, survival time from a disease, income per-capita etc. Thus use of probability models with support on finite intervals becomes inevitable. The authors have investigated stress-strength reliability based on unit GR distribution. Useful comments are obtained based on the numerical study.

The research examines how the principle of Dharma can be included in the Indian National Education Policy (NEP) 2020. The study seeks to know how Dharma as an educational framework can be infused with contemporary education to promote moral and ethical development as well as intellectual growth among students in India.

This research uses qualitative analysis of NEP 2020 documents and literature related to Indian Knowledge Systems (IKS) and the concept of Dharma. It analyses the philosophical bases behind NEP including traditional texts like Vedas, Upanishads, among others. Moreover, this study also evaluates how current Dharma teachings are implemented into curriculum by assessing its present status of incorporation as a part of Indian Knowledge System.

This research has found that national educational reforms have considered many traditional Indian beliefs and values, but they have not done enough when it comes to incorporating Indian Knowledge System and the concept of Dharma into practice through the curriculum. The paper advocates for a system that is grounded on morals, thus blending the current learning standards with cultural heritage. These findings call for continuous endeavouring in order to embed ethical and moral dimensions of Dharma across all levels within India’s education systems.

The integration of Dharma and IKS in education can enhance the development of well-rounded individuals who are not only intellectually competent but also ethically and morally grounded. This strong foundation of Dharma will also act as a personal and professional guide. Educators and policymakers can use these insights to design curricula that promote holistic growth, aligning with both global educational standards and cultural values.

This paper examines the philosophical and ethical foundation of NEP 2020 instead of only considering its pedagogical and skill-enhancement characteristics, thereby giving a fresh viewpoint. It contributes to the discussion on educational reforms in India by highlighting how present educational needs should be linked with traditional values. The research shows that the concept of dharma can help learners develop holistically so that they can meet present-day problems without losing touch with timeless morals.

The purpose of this paper is to present an analytical drain current model for output characteristics of strained‐Si/SiGe bulk MOSFET.

A physics‐based model for current output characteristics and transconductance of strained‐Si/SiGe bulk devices has been developed incorporating the impact of strain (in terms of equivalent Ge mole fraction), strained silicon thin film thickness, gate work function, channel length and other device parameters. The accuracy of the results obtained using this model is verified by comparing them with 2D device simulations.

This model correctly predicts the output characteristics, I_DS−V_GS characteristics, transconductance and output conductance of the strained‐Si/SiGe MOSFET and demonstrates a significant enhancement in the drain current of the MOSFET with increasing strain in the strained‐Si thin film, i.e. with increasing equivalent Ge concentration in the SiGe bulk.

Can be implemented in a SPICE like simulator for studying circuit behaviour containing strained‐Si/SiGe bulk MOSFETs.

The model discussed in this paper can be easily implemented in a circuit simulator and used for the characterization of strained silicon devices. This complements the recent trend of investigation of new materials and device structures to maintain the rate of advancement in VLSI technology.

This paper presents, for the first time, a compact surface potential‐based analytical model for strained‐Si/SiGe MOSFETs which predicts the device characteristics reasonably well over their range of operation.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

This study aims to understand the kind of content and context that effectively create higher fan social media engagement (SME) through pre-match content posted by sports teams.

This research examines the effect of inspirational, informational, entertaining and warmth content appeal on affective and cognitive responses from fans in the form of likes and shares. Messages on X (previously Twitter), chosen as a representative social media platform, from the teams participating in the Indian Premier League, were analysed using regression models to validate the proposed model empirically.

For sports clubs, entertaining, warmth and inspirational content is more effective than information content in generating likes on social media. Content with high vividness is effective only for sports teams with high performance. Fans of low-performance teams exhibit higher responsiveness to content with inspirational appeal.

This research contributes to the sports marketing literature by examining the influential role of warmth and inspirational content in generating higher SME in the pre-match context.

This study provides prescriptions to sports clubs for leveraging social media platforms to engage their fans through appropriate content. Given the growth of sports leagues in developing and developed countries, this study provides guidelines to sports clubs for effective social media marketing.

To the best of the authors’ knowledge, this study is among the first to integrate social identity theory and elaboration likelihood model theoretical frameworks to study fan engagement with social media content posted by sports clubs.

After completing the case, the students shall be able to do the following:▪ explain the key features and roles associated with non-governmental organizations (NGOs; comprehension);▪ describe major activities/programs run by Kilkaari-Bachpan Ki (knowledge);▪ describe the challenges faced by not-for-profit, non-governmental and voluntary organizations operating at a local level (knowledge);▪ developing TOWS matrix for Kilkaari’s strengths, weaknesses, threats and opportunities to provide strategies for making the necessary change (analysis);▪ explain the change management strategies used by Kilkaari during COVID-19 using Kurt Lewin’s model (analysis); and▪ develop recommendations for the problems faced by Kilkaari (evaluation).

As a result of the global COVID-19 pandemic, the entire world was left in shambles. The epidemic has had a profound impact on the way organizations used to function. It compelled all sectors and companies to change their working methods and move closer to virtual conceptions. Organizations that were able to adapt to the new environment have thrived, while the rest have struggled and continue to struggle. In today’s world, embracing virtual scenarios is more about perseverance and consistency than it is about making a conscious decision. Organizations such as NGOs have limited financial and non-financial means to adapt to such changes, and some of them were unable to suffice during the tough times.Despite this, the current case study focuses on the NGOs’ path during the pandemic: Kilkaari-Bachpan Ki. The NGO’s primary objective is to provide underprivileged children with free access to education. The current scenario illustrates how the Kilkaarians (team members of Kilkaari) modified their behavior in response to the crisis. To be used in an organizational behavior course, the case is intended for management students. Kurt Lewin’s model of change, which includes unfreezing, mobility and refreezing, can be understood by students. For this research, the authors use a case-study approach to explore how the organization responded when faced with a crisis.

This case is suitable for undergraduate students learning organizational behavior course, strategic management and also relevant to social work courses (social entrepreneurship/NGOs).

Teaching notes are available for educators only.

CSS 6: Human resource management.