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Purpose: The COVID-19 pandemic has been a good example of a Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) world. Higher educational institutions (HEIs) have faced a massive hit because the jobs in this industry have become unexpected. Considering the most valuable assets ‘Teachers’ crunched in the VUCA crisis, the study intends to determine if personal harmony (PH) and organisational citizenship behaviour (OCB) would enhance teachers’ job satisfaction (JS).

Design/methodology/approach: Data are collected from the teachers of Indian HEIs and teachers who have experienced the impact of the COVID-19 catastrophe (VUCA). Considering the pandemic restrictions, data have been collected through an online survey (N = 364).

Practical Implications: PH is an individual’s internal quality and attribute that cannot be developed on force or situational need. Even in an uncertain situation, teachers have tried their best to contribute through professional service. Hence, people who possess PH contribute their best even though unsatisfied with their jobs.

Originality/value: This study has focused on finding the relationship between two different variables, PH and OCB (which has not been explored in Asian countries, majorly in India, where it has a vast cultural diversity and structure influencing the educational policies) that hinders the factors influencing JS, where these two variables are highly influenced by hygiene factors such as values, culture, ethical standards, personal belief, leadership styles, and fair treatment showcased by the organisations/institutions.

This introduction aims to set the context for the subsequent chapters that problematize various aspects of social justice, equity, and inclusion through particular lenses, and/or methodologies. This is done by presenting the ‘problem’ of social justice and equity in education, while simultaneously making links with the Sustainable Development Goals (SDGs). The term ‘social justice’ is appearing in numerous public texts and discourses within the education field, thus becoming a key concept in current education policy and practice. Moreover, the concept of social justice is crucial to theorizing about education and schooling, consequently being considered by politicians, policymakers, and practitioners in their thinking about the nature of education and the purpose of schools. Regrettably, education practitioners, researchers, and policymakers often utilize this umbrella term (social justice) while leaving out salient details about its social, cultural, economic, and political bearing. Notwithstanding the unanimous agreement on the desirability of social justice as an educational goal, this is complemented by a parallel contestation over its actual meaning and application in relation to schooling, that is, in relation to the formulation of policy and how it is to be included in practice. This chapter seeks to unravel the conceptual confusion around the terms social justice, equity, and inclusion in relation to schooling and education, through an exploration of the existing literature in the field.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.