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This research explores the antecedents of tourist satisfaction and revisit intention, especially in light of the post-pandemic environment that pushed tourism to the lowest level in the present decade. A comprehensive conceptual model based on the brand equity theory (BET) is developed, and interrelationships between brand heritage, community support and benefits, hedonism, culture, tourist satisfaction and revisit intention.

The data were collected from 670 tourists visiting one of the heritage destinations in southern India (Kanchipuram). After checking the psychometric properties of the survey instrument, the hypotheses were tested using path analysis and Hayes's PROCESS macros.

The results indicate that heritage brand of destination sites is positively associated with (1) tourist satisfaction and (2) tourist revisit intention. The findings also support the positive effect of attractiveness of products is positively related to tourist satisfaction, which in turn, is a significant predictor of tourist revisit intention. The findings also support that community support benefits moderating the relationship between heritage brand and attractiveness of products in heritage destinations tourist satisfaction.

This study has several implications for practicing managers engaged in maintaining heritage destinations. As this research highlights the importance of heritage brands of destinations in influencing tourist satisfaction, local communities and policymakers need to provide the necessary infrastructure and facilities at these destinations. Further, local entrepreneurs are motivated to invest in delivering products that attract tourists.

The conceptual model developed in this study, is first of its kind, to the best of the authors’ knowledge, that investigates the effect of hedonism (second moderator) moderating the relationship between tourist satisfaction and culture (first moderator) in influencing tourists to revisit their intention. In addition to direct relationships, the moderating role of community support and benefits, culture and hedonism makes a unique contribution to the bourgeoning literature on tourism especially in Indian context.

In the face of escalating urban populations, the quest for seamless mobility in cities becomes increasingly complex, even in regions where transit options are presumably accessible within the developing world. The imperative to confront urban mobility challenges and forge sustainable cities equipped with adept transportation and traffic management systems cannot be overstated. This study delves into the technological paradigms employed by developed nations and evaluates their pertinence in the current milieu for mitigating urban mobility challenges. Simultaneously, it scrutinizes the deployment of smart city technologies (SCTs) within developing nations, investigating potential technological strides that can be harnessed to achieve sustainable urban transportation. By dissecting the intricacies of SCTs in developing countries, the study aims to unearth viable technological advancements that can be judiciously implemented to foster sustainable urban mobility. It aspires to provide nuanced recommendations for the integration of latent SCTs, unlocking untapped potential to augment the sustainability of urban transportation in the developing world. The research also elucidates strategies geared towards fostering international collaborations which are instrumental in propelling the development of cities characterized by equity and inclusivity. The study underscores the significance of a global alliance in overcoming urban challenges, emphasizing the need for shared knowledge, resources and experiences to propel the evolution of cities towards a more sustainable and equitable future. This research serves as a comprehensive exploration of the intricate interplay between technology, urbanization and international cooperation, offering insights and recommendations pivotal to steering the trajectory of urban development in developing nations.

The main objective of the paper is to find evidence of abnormal returns and performance persistence of actively managed equity funds in the Indian context on an annual basis during the post-subprime crisis period between 2009 and 10 and 2019 and 2020.

The study is exploratory and empirical, used daily net asset value (NAV) of 180 equity funds for 10 years and applied the risk-adjusted, Jensen's (1968) single-factor, Fama and French's (1993) three-factor model and Carhart's (1997) four-factor model to evaluate the performance. The performance persistence has been tested using cross-section regression (Bollen and Busse, 2005), the non-parametric contingency approach, along with the robustness measure, i.e. Malkiel's (1995) Z-score, Brown and Goetzmann's (1995) cross-product ratio (CPR) and Kahn and Rudd's (1995) χ² value.

The results show that the Indian equity funds are unable to generate abnormal returns, and the size, value and momentum strategies applied by the fund managers in generating abnormal returns do not work effectively. However, funds provide strong evidence of significant performance persistence on an annual basis in the short-term, mid-term and long-term periods. Both parametric as well as non-parametric tests provide identical evidence of persistence, and the performance persistence is independent of the choice of models, as all the models (i.e. two, three or four-factor models) provide significant evidence of persistence.

Though the study is comprehensive and covered a longer period, there is a scope for future research by examining the influence of fund characteristics, fund rating and macroeconomic factors on performance and persistence. It can be extended over to a longer period covering the post-COVID-19 period, a larger sample size and a comparative study of Indian and foreign mutual funds (MFs).

The outcomes of this research paper can help wealth-maximizing investors in the identification of persistent equity funds and can apply the previous period’s performance information as a useful investment strategy to generate higher returns in the future. We believe that these outcomes will have significant ramifications for all MF stakeholders and policymakers, especially for the Indian industry in ensuring and establishing the credibility of MF managers, in providing better returns as well as to make MF investment more attractive to Indian retail investors.

Despite the exponential growth in the Indian MF industry, limited evidence is available on performance and persistence covering a large sample size during the post-sub-prime crisis period using different return models and parametric and non-parametric approaches. The study is based on the daily data set of a larger sample size representing all the Asset Management Company (AMC) and the longer period following the post-subprime crises, which affected capital flows significantly. Moreover, the application of all the measures enables us to understand performance persistence in a larger context.

This study aims to assess the effect of water variation on bioethanol production from cassava peels (CP) using Saccharomyces cerevisiae yeast as the ethanologenic agent.

The milled CP was divided into three treatment groups in a small-scale flask experiment where each 20 g CP was subjected to two-stage hydrolysis. Different amount of water was added to the fermentation process of CP. The fermented samples were collected every 24 h for various analyses.

The results of the fermentation revealed that the highest ethanol productivity and fermentation efficiency was obtained at 17.38 ± 0.30% and 0.139 ± 0.003 gL⁻¹ h⁻¹. The study affirmed that ethanol production was increased for the addition of water up to 35% for the CP hydrolysate process.

The finding of this study demonstrates that S. cerevisiae is the key player in industrial ethanol production among a variety of yeasts that produce ethanol through sugar fermentation. In order to design truly sustainable processes, it should be expanded to include a thorough analysis and the gradual scaling-up of this process to an industrial level.

This paper is an original research work dealing with bioethanol production from CP using S. cerevisiae microbe.

1. Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

2. Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

3. Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

4. Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

School teachers had difficulty in monitoring and assessing the students during the COVID-19 lockdown. This study attempts to understand the assessment and teaching challenges during COVID-19 and how this problematic situation was reshaped by new normal teaching.

The qualitative research included perspectives of 84 school teachers using an interview-based approach. Pedagogical assessment, societal assessment and personal assessment were considered in the study.

Content switching, student involvement and individual assessment are the challenges in terms of pedagogy. Buying power of electronic equipment, digital skills, study environment and distraction are the challenges faced in terms of personal challenges. Personal connect and interaction intensity is the challenges faced in terms of social challenges. Teachers adapted to synchronous, blended, flipped and asynchronous modes of teaching.

The authors’ study will support schools in developing their institutional plans to understand teachers' apprehensions better and to check the readiness of the schools towards teaching and learning approaches.

The purpose of this study is to extract and characterize a novel natural dye from the leaves of Lannea coromandelica and the extraction with finding ways of dyeing cotton fabric using three mordants.

The colouring agents were extracted from the leaves of Lannea coromandelica using an aqueous extraction method. The extract was characterized using analysis methods of pH, gas chromatography-mass spectrometry (GC-MS), Fourier transform infrared (FTIR), ultraviolet-visible (UV-vis) and cyclic voltammetry measurement. The extract was applied to cotton fabric samples using a non-mordant and three mordants under the two mordanting methods. The dyeing performance of the extracted colouring agent was evaluated using colour fastness properties, colour strength (K/S) and colour space (CIE Lab).

The aqueous dye extract showed reddish-brown colour, and its pH was 5.94. The GC-MS analysis revealed that the dye extract from the leaves of Lannea coromandelica contained active chemical compounds. The UV-vis and FTIR analyses found that groups influenced the reddish-brown colour of the dye extraction. The cyclic voltammetry measurements discovered the electrochemical properties of the dye extraction. The mordanted fabric samples showed better colour fastness properties than the non-mordanted fabric sample. The K/S and CIE Lab results indicate that the cotton fabric samples dyed with mordants showed more significant dye affinities than non-mordanted fabric samples.

Researchers have never discovered that the Lannea coromandelica leaf extract is a natural dye for cotton fabric dyeing. The findings of this study showed that natural dyes extracted from Lannea coromandelica leaf could be an efficient colouring agent for use in cotton fabric.

This study aims to investigate the impact of implementing various robotic systems in logistics and supply chain management on corporate performance in Jordanian manufacturing companies, focusing on environmental, operational, economic, and social dimensions.

Using a quantitative approach, data was collected through a survey questionnaire to measure the relationship between robotic systems and several performance dimensions. Various established constructs were employed, and the structural relationships were analyzed using partial least squares structural equation modeling (PLS-SEM) to assess the complex interdependencies within the model.

The results of this study indicate that the adoption of robotic systems has a positive influence on the environmental, operational, economic, and social performance of Jordanian manufacturing companies. In contrast to prior research that revealed inconsistencies in the social dimension, our findings align with previous studies highlighting the benefits of robotics in logistics and supply chain management. However, it’s worth noting that this study did not uncover similar inconsistencies, particularly in terms of the impact on social performance.

The study provides valuable insights for manufacturing companies considering the implementation of robotic systems, highlighting the need to evaluate the environmental, operational, social, and economic consequences. This understanding can help organizations make informed decisions to leverage the benefits of robotics for sustainable growth.

This study contributes to the growing literature on robotics in logistics and supply chain management, specifically focusing on the unique context of Jordanian manufacturing companies. By examining the multifaceted impact of robotic systems, this study extends the understanding of the role of technology in enhancing corporate performance.

Composite materials have revolutionized the aerospace industry by offering superior structural qualities over traditional elements. This study aims to focus on the development and testing of bamboo natural fiber-based composites enhanced with SiO₂ nanoparticles.

The investigation involved fabricating specimens with varying nanoparticle compositions (0, 10 and 20%) and conducting tensile, flexural, impact and fracture toughness tests. Results indicated significant improvements in mechanical properties with the addition of nanoparticles, particularly at a 10% composition level.

This study underscores the potential of natural fiber composites, highlighting their environmental friendliness, cost-effectiveness and improved structural properties when reinforced with nanoparticles. The findings suggest an optimal ratio for nanoparticle integration, emphasizing the critical role of precise mixing proportions in achieving superior composite performance.

The tensile strength, flexural strength, impact resistance and fracture toughness exhibited notable enhancements compared with the 0 and 20% nanoparticle compositions. The 10% composition showed the most promising outcomes, showcasing increased strength across all parameters.

Construction projects in Malaysia are often delayed and over budget due to heavy reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) studies, which are less accurate than machine learning (ML) tools. Construction management applications have increasingly used ML tools in recent years and have greatly impacted forecasting. The research aims to identify the most influential LC factors using statistical approaches, collect data and forecast LC models for improved forecasts of LC.

A thorough literature review was completed to identify LC factors. Experienced project managers were administered to rank the factors based on importance and relevance. Then, data were collected for the six highest ranked factors, and five ML models were created. Finally, five categorical indices were used to analyze and measure the effectiveness of models in determining the performance category.

Worker age, construction skills, worker origin, worker training/education, type of work and worker experience were identified as the most influencing factors on LC. SVM provided the best in comparison to other models.

The findings support data-driven regulatory and practice improvements aimed at improving labor issues in Malaysia, with the possibility for replication in other countries facing comparable problems.

This paper evaluates the benefits of harnessing artificial intelligence (AI) tools for safety compliance on construction projects in Nigeria.

This study employed a specialised approach by combining qualitative and quantitative approach. The study carried out a brief systematic literature review (SLR) to identify the variables of the study. These variables were prepared in a questionnaire which was distributed among professionals within the Nigerian construction sector using purposive sampling. A total of 140 questionnaires were retrieved. The collected data were analysed using Relative Importance Index (RII), Ginni’s Mean (GM) and exploratory factor analysis (EFA).

The analysis revealed that all the identified benefits hold considerable importance, with an average RII of 0.86, with real-time monitoring as the most prominent advantage. However, using the GM which was 0.861, the study identified “mitigation of hazards on worksites” as the stationary benefit of AI in safety compliance.

The study was conducted exclusively within Nigeria’s Federal Capital Territory, using a cross-sectional survey approach.

The results will be valuable for professionals and practitioners in the Nigerian construction sector, as they will acquire insights into the potential advantages of utilising AI tools for monitoring of safety compliance on construction projects.

The study adopted a robust approach by identifying the stationary benefit using the GM in combination with RII and EFA.