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This study aims to present an innovative approach to detect and monitor ethylene gas during fruit ripening.

It uses a specialized composite membrane in conjunction with a solid-state electrochemical method. This unique electroactive membrane, composed of polyvinyl alcohol (PVA), chitosan (CHT), lithium chloride (LiCl) and ammonium molybdate (AMO), exhibits synergistic behavior when applied to a microelectrode chip surface. This composite enhances the sensitivity of electrochemical ethylene detection. Empirical experiments were conducted to elucidate the ripening kinetics in various fruit specimens, including apples, pears and mangoes. These fruits released ethylene, which was analyzed using the molybdenum-permeated electroactive biopolymer composite membrane, a critical determinant of ethylene levels.

Characterization of the synthesized composite through techniques such as X-ray diffraction and Fourier-transform infrared spectroscopy revealed reduced crystallinity and decreased hydrogen bond interactions upon activation with Mo ions. Field emission scanning electron microscopy images exhibited a distinctive porous surface morphology with spherical microgranules. Energy dispersive X-ray analysis indicated a significant change in the mass or atomic composition of Mo in the composite membrane after Mo ion activation. Electrochemical measurements, including cyclic voltammetry and potentiostatic electrochemical impedance spectroscopy, validated the efficiency of the Mo-activated PVA-CHT-LiCl-AMO membrane, manifesting an impressive 87.79% increase in sensitivity compared to the nonactivated membrane.

This research work represents a significant advancement in the field of ethylene detection and fruit ripening monitoring. The Mo-activated PVA-CHT-LiCl-AMO membrane offers a reliable and effective solution for real-time ethylene detection, providing an invaluable tool for the horticultural industry to optimize fruit ripening processes, extend shelf life and ensure the delivery of high-quality produce to consumers.

The findings of this study hold great promise for fostering sustainability and efficiency within the global fruit supply chain, ultimately benefiting both producers and consumers alike.

The implications of this research extend to the fabrication of a sensor based on a solid-state electroactive PVA-CHT-LiCl-AMO composite membrane, which upon Mo-activation exhibits robust electrochemical fruit ethylene detection when exposed to different fruits.

This study aims to investigate the use of 20 commonly applied regression methods to predict concrete corrosion. These models are assessed for accuracy and interpretability using SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analysis to provide structural health monitoring prognostic tools.

This study evaluated model performance using standard measures including root mean square error (RMSE), mean square error (MSE), R-squared (R²) and mean absolute error (MAE). Interpretability was evaluated using SHAP and LIME. The X and Y distances, concrete age, relative humidity and temperature were input parameters, whereas half-cell potential (HCP) values were considered output. The experimental data set consisted of observations taken for 270 days.

Gaussian process regression (GPR) models with rational quadratic, square exponential and matern 5/2 kernels outperformed others, with RMSE values around 16.35, MSE of roughly 267.50 and R² values near 0.964. Bagged and boosted ensemble models performed well, with RMSE around 17.20 and R² values over 0.95. Linear approaches, such as efficient linear least squares and linear SVM, resulted in much higher RMSE values (approximately 40.17 and 40.02) and lower R² values (approximately 0.79), indicating decreased prediction accuracy.

The findings highlight the effectiveness of GPR models in forecasting corrosion in concrete buildings. The use of both SHAP and LIME for model interpretability improves the transparency of predictive maintenance models, making them more reliable for practical applications.

Safe infrastructure is crucial to public health. Predicting corrosion and other structural problems improves the safety of buildings, bridges and other community-dependent structures. Public safety, infrastructure durability and transportation and utility interruptions are improved by reducing catastrophic breakdowns.

This study reduces the gap between model accuracy and interpretability in predicting concrete corrosion by proposing a data-driven method for structural health monitoring. The combination of GPR models and ensemble approaches provides a solid foundation for future research and practical applications in predictive maintenance. This comprehensive approach underscores the potential of data-driven methods for predictive maintenance in concrete structures, with implications for broader applications in various industries.

The purpose of this research is to examine how the implementation of a balanced scorecard (BSC) affects business outcomes in both information technology (IT) and non-IT sectors.

Partial least squares structural equation modeling (PLS-SEM) was used to test the hypothesis. A random sample was used to collect 170 responses from the IT companies and 166 from non-IT companies by using the questionnaire method. The questionnaire was distributed to the top- and middle-level managers in Bangalore city, and we used SmartPLS software to explore the relationship between our research constructs.

The results of this study indicate that a BSC has a significant and positive impact on organizational performance in IT and non-IT sectors. The main distinction in this study is that all BSC perspectives [learning and growth perspective, internal business process (IBP) perspective, customer perspective (CP) and financial perspective (FP)] have a significant, direct and indirect impact on IT companies. On the other hand, solely three BSC perspectives (IBP perspective, CP and FP) have a significant impact on non-IT companies, while learning and growth perspective has an insignificant impact on the FP.

This study provides a critical theoretical and practical contribution of a BSC on business performance in IT and non-IT industries.

This paper aims to explore the outcomes of an analysis on day by day task – journey planning conduct of senior citizens by using a modern dynamic model and a family unit travel overview, gathered in Bhubaneswar, Odisha, of India in 2018. The task-journey planning display assumes an unique time–space-constrained planning development.

The main commitment of this paper is to reveal day by day task – journey planning conduct through a comprehensive dynamic framework. Numerous behavioural subtleties are revealed by the subsequent empirical model. These incorporate the role that income plays in directing outside time consumption decisions of senior citizens. Senior citizens in the most elevated and least salary classes will in general have minor varieties in time consumption decisions than those in middle pay classifications. Generally speaking, the time consumption decisions become progressively steady with expanding age, demonstrating that more task durations and lower task recurrence become progressively predominant with increasing age.

Day by day task-type and area decisions reveal a reasonable irregular utility-amplifying level headed conduct of senior residents. Unmistakably expanding spatial availability to different task areas is an urgent factor in characterizing every day outside task interest of senior residents. It is likewise evident that the assorted variety of outside task-type decisions decreases with rise in age and senior citizens are major touchy to auto journey hour than to travel or non-mechanized journey hour.

The fundamental constraint to the dynamic structure is that the mode decision model was viewed as exogenic to the demonstrating framework. The essential purpose behind this supposition that was that senior citizens in the Bhubaneswar are overwhelmingly customers of the local car. Coordination of the mode decision display part inside this structure would deliver a full task-based journey request model that could catch trip age, starting times, outing circulation and mode decision using a solitary demonstrating framework.

India’s manufacturing sector employs about 12% of the labour force and contributes to about 17% of the nation’s GDP. The Indian government intends to implement several initiatives under the “Make in India” and Atma Nirbhar Bharat banners to increase the manufacturing sector’s share of the nation’s GDP to 25% by 2025. Applying lean manufacturing, green manufacturing and Six Sigma is crucial to ensure that India’s manufacturing sectors grow sustainably in international markets. This study aims to identify sustainability indicators and ascertain their respective weights to evaluate the sustainability performance of the Indian manufacturing industry.

This research identifies 25 sustainability indicators and classifies them into the triple bottom line of sustainability based on an evaluative literature review and expert opinion. The Best Worst Method was utilised to determine the weights of the sustainability indicators. The sustainability index was developed to evaluate economic, social and environmental sustainability.

The sustainability performance of a foundry in a significant Western Indian State city was assessed by applying the developed sustainability index. After the adoption of integrated lean, green and Six Sigma (LG&SS) strategies and related practices in the foundry, there has been a notable improvement of 68.03% in the economic index, 61.62% in the social index and 13.24% in the environmental index.

The proposed sustainability index is applied and evaluated specifically for assessing the sustainability performance of Indian manufacturing SMEs. It can be used to substantiate firm’s sustainability performance and also to assess the improvement in firm’s performance in economic, environmental and social dimensions after implementing various operational excellence practices. However, it cannot serve as a benchmark tool across similar companies or organisations.

The developed sustainable index can be used to analyse the company or organisation’s sustainability performance and see how various strategies have improved things. Practitioners can use this index to assess social, economic and environmental performance and focus on areas that need improvement.

The proposed sustainability index serves as a vital tool for monitoring a firm’s progress in triple bottom line (TBL) dimensions of sustainability, tracking a diverse range of indicators and encouraging sustainable organisational practices.

This study attempts to assess the economic, social and environmental performance of Indian Manufacturing SMEs by proposing a sustainability index.

The purpose of this paper is to provide an organizing lens for viewing the distinct contributions to knowledge production from those research communities addressing the impact of competitive strategy on company performance in general, and the influence of cost leadership and differentiation strategy on organizational performance in detail.

The research methodology was based on the PRISMA review, and thematic analysis based on an iterative process of open coding was analyzed and then the sample was analyzed by illustrating the research title, objectives, method, data analysis, sample size, variables and country.

The main factor that influenced the competitive strategy is strategic growth; strategic growth has a significant influence on competitive strategy. Furthermore, competitive strategy will boost firm network, performance measurement and organization behavior. In the same way, the internal goal factor will enhance organizational effectiveness. Also, a differentiation strategy will support management practice factors, strategic positions, product price, product characteristics and company performance.

This study contributes to the literature by identifying a framework of competitive strategy factors, company performance factors, cost leadership strategy factors, differentiation strategy factors and competitive strategy with global market factors. This study provides a complete picture and description of the resulting body knowledge in competitive strategy and organizational performance.

The emergence of virtual reality (VR) has the potential to revolutionize various industries, including tourism, as it delivers a simulated environment that closely emulates real-life experiences. Therefore, this study aims to explore how the factors, i.e. enjoyment, emotional involvement, flow state, perceived privacy risk, physical risk and cost, influence the customers’ intention to use VR for tourism.

This study integrates the technology acceptance model, hedonic consumption theory with other factors, including cognitive response, authenticity, perceived privacy risk, perceived physical risk, perceived cost and perceived presence. Partial least squares structural equation modelling approach was used to test the proposed research model.

The finding based on the sample of 252 respondents revealed that authenticity is the most influential factor impacting behavior intention followed by perceived cost, attitude, cognitive response and enjoyment. Also, the study supported the moderating impact of personal innovativeness between attitude and behavioral intention to use VR for tourism.

The findings of the study offers practical implications for service providers, site managers, destination marketers, tourist organizations and policymaker to develop more effective strategies for offering VR services for tourism.

This study enriches the current understanding of VR adoption in context of tourism with empirical evidences.

This study investigates the intricate dynamics of the hidden curriculum and its influence on student achievement in teacher education programs. Specifically, the study examines the mediating role of professional and constitutional values and the moderating role of sex in this relationship.

A total of 282 bachelor of education fourth semester students (151 female and 131 male) participated in the study. General linear modeling in Jamovi 2.4.4 was utilized to analyze the data. The general linear model (GLM) mediation model was used to measure the mediation effects of the hidden curriculum via professional value and constitutional value on student achievement. Sex was accepted as a dichotomous moderator in the model.

Hidden curriculum had positive significant direct and total effect on student achievement. The mediation of constitutional values between hidden curriculum and student achievement was found to be significant and negative. Professional value was not found to be a significant mediator. The interaction between sex and constitutional value was significant and positive. Apart from this sex moderated professional and constitutional values differentially. Constitutional and professional values had significant direct component effects on student achievement. The findings contribute to the discourse of hidden curriculum by providing insights into the interplay of sex, professional values, constitutional values and student achievement.

The study has implications for educational policies and curriculum design, urging stakeholders to pay attention to the implicit messages communicated by the curriculum. Professional and constitutional values influence the hidden curriculum and achievement. Therefore, teachers should be equipped with an understanding of these values to manage the hidden curriculum in their classrooms. The study also highlights the gendered educational experiences and the necessity of implementing gender-sensitive policies and practices in schools and classrooms.

This study provides unique insights into the complex relationship between the hidden curriculum and student achievement in the context of a teacher education program in India. The findings highlight the importance of addressing the hidden curriculum in educational settings and emphasize the need for gender-sensitive policies and practices.

A nation's economic development is adversely affected by the rising population and the lack of employment opportunities, necessitating the promotion of entrepreneurial activities. This study aims to unfurl the critical factors shaping university students' entrepreneurial intentions (EI) in an emerging economy.

The study assessed the EI of 640 university students across India's top 5 central universities through structural equation modeling by extending the entrepreneurial event model (EEM) and dark triad theory. Mediation and moderation techniques were used to examine the model's direct and indirect linkages.

The results show that propensity to act (PTA), perceived resilience (PRS) and narcissism (NAR) magnify desirability, significantly increasing students' EI. The association between the underlying constructs is partially mediated by the PTA and perceived desirability (PD). In addition, the linkage between PD and students' EI is strengthened by the pursuit of excellence (PoE) and weakened by perceived risk (PR).

The study's findings could assist educators, universities, aspiring entrepreneurs, financial institutions, policymakers, investors, venture capitalists, incubators and accelerators in reinforcing entrepreneurial culture in India by establishing a link between crucial personality traits and intentions to start entrepreneurial ventures.

Personality traits such as NAR, resilience and the PoE have not received much attention in the entrepreneurship literature and call for a rigorous inquiry. The study tries to embrace these dominant personality traits by broadening the perspectives of EEM and dark triad theory via a comprehensive conceptual model. It also explores the role of the PoE and PR as moderators to examine the possible association between the identified constructs.

Despite India achieving many milestones under MGNREGA, the external and internal shocks result in below potential outcomes in employment demand and generation. This study examines how these shocks matter and how the migration-prone regions perform.

This study, employing district-level data from 2018 to 2021, investigates how climate change and COVID-19 have affected the employment demand and supply. We applied RE-GLS and IV-2SLS regressions to examine the effects of shocks on employment demand and generation, respectively. The difference in difference panel model is employed to test the spatial effects of the pandemic. Further, we used RE-GLS and extended regression model to examine how external shocks interacting with migration affect unemployment rates.

It was found that the pandemic increased employment demand and generation. This reflects the adverse effects of the pandemic and the swift action by the government. However, the responses were possibly different during climate shocks. The wage differential increased employment demand. However, demand decreased due to poor support from the support staff. The employment generation was higher in migration-prone districts, indicating that seasonal migration, being a lean-season phenomenon, continues to occur despite employment generation.

This study contributes to the literature in several ways. It captures spatial variations while examining the impact of climate change and COVID-19. It investigates the performance of MGNREGA in migration-prone areas. In effect, the findings provide policymakers with greater insight into the issues.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0132.